ai architect

Cognizant

About AI & Analytics: Artificial intelligence (AI) and the data it collects and analyzes will soon sit at the core of all intelligent, human-centric businesses. By decoding customer needs, preferences, and behaviors, our clients can understand exactly what services, products, and experiences their consumers need. Within AI & Analytics, we work to design the future—a future in which trial-and-error business decisions have been replaced by informed choices and data-supported strategies.
 By applying AI and data science, we help leading companies to prototype, refine, validate, and scale their AI and analytics products and delivery models. Cognizant’s AIA practice takes insights that are buried in data and provides businesses a clear way to transform how they source, interpret and consume their information. Our clients need flexible data structures and a streamlined data architecture that quickly turns data resources into informative, meaningful intelligence.
 <ul>
<li>Please note, this role is not able to offer visa transfer or sponsorship now or in the future*</li>
</ul>
Job Summary
 We are seeking an AI Architect to lead the design, development, and delivery of enterprise-scale Generative AI and agentic AI solutions on Google Cloud Platform. This role focuses on defining end-to-end AI/ML architecture strategy, building scalable LLM and multi-agent systems, and driving innovation using Vertex AI. You will play a key role in enabling intelligent automation through advanced AI frameworks while ensuring production readiness, governance, and business alignment. The position requires strong technical leadership, deep hands-on expertise, and client-facing consulting experience.
 
In this role, you will: 
<ul>
<li>Define and lead enterprise AI architecture strategy for Generative AI and agentic AI solutions on GCP.</li>
<li>Architect and implement LLM-based applications using Vertex AI services including Model Garden, Gemini API, and custom training pipelines.</li>
<li>Design and develop multi-agent AI systems using Google ADK or equivalent frameworks (LangGraph, CrewAI, AutoGen).</li>
<li>Build scalable Retrieval-Augmented Generation (RAG) pipelines using Vertex AI Search, Vector Search, and BigQuery.</li>
<li>Develop and manage LLMOps pipelines covering model versioning, evaluation, fine-tuning (SFT/RLHF/PEFT), and deployment using Vertex AI Pipelines.</li>
<li>Leverage GCP-native services (Cloud Run, GKE, Dataflow, Pub/Sub, Apigee) to design and implement AI-first platforms.</li>
<li>Provide technical leadership, mentor teams, and conduct architecture reviews ensuring governance and responsible AI practices.</li>
<li>Define reusable design patterns, best practices, and reference architectures for LLM and agentic AI solutions.</li>
<li>Collaborate with clients and stakeholders to define AI strategy, lead PoCs, solution demos, and RFP/RFI responses.</li>
<li>Drive innovation adoption by evaluating emerging AI tools, models, frameworks, and enterprise AI capabilities.</li>
</ul>
What you need to have to be considered
 <ul>
<li>15+ years of experience in AI/ML, cloud architecture, or enterprise digital transformation roles.</li>
<li>Strong hands-on expertise in Google Cloud Platform including Vertex AI, BigQuery, Dataflow, GKE, Cloud Run, Pub/Sub, and IAM.</li>
<li>Proven experience designing and delivering production-grade Generative AI and LLM-based applications.</li>
<li>Deep knowledge of RAG architectures, embeddings, vector databases, prompt engineering, and model evaluation strategies.</li>
<li>Experience building agentic AI systems using ADK or frameworks like LangGraph, CrewAI, or AutoGen.</li>
<li>Strong programming skills in Python with experience in MLOps tools (Vertex AI Pipelines, MLflow, Kubeflow).</li>
<li>Solid understanding of containerization (Docker, Kubernetes) and CI/CD pipelines.</li>
<li>Strong leadership, stakeholder engagement, and consulting experience working with enterprise clients.</li>
<li>GCP certifications (Professional Architect / ML Engineer) and experience in regulated industries are a plus.</li>
</ul>
#LI-EF1
 #CB
 #Ind123
 Applications will be accepted until 10 Jul 2026.
 
Salary and Other Compensation: 
The annual salary for this position is between $[174,500 - 204,500] depending on experience and other qualifications of the successful candidate.
 This position is also eligible for Cognizant’s discretionary annual incentive program, based on performance and subject to the terms of Cognizant’s applicable plans.
 Benefits: Cognizant offers the following benefits for this position, subject to applicable eligibility requirements:
 <ul>
<li>Medical/Dental/Vision/Life Insurance</li>
<li>Paid holidays plus Paid Time Off</li>
<li>401(k) plan and contributions</li>
<li>Long-term/Short-term Disability</li>
<li>Paid Parental Leave</li>
<li>Employee Stock Purchase Plan</li>
</ul>

Sr. AI Architect

The Cigna Group

<div>About Cigna Healthcare<h3 class="jobSectionHeader">
Cigna Healthcare is a global health service company dedicated to transforming healthcare. With roots in the U.S. and operations in over 30 countries, we serve more than 180 million customers and patients worldwide. Ranked 13th on the Fortune 500 in 2025, Cigna is recognized as one of the most trusted and influential names in the industry.</h3><h3 class="jobSectionHeader">
Our mission is to improve the health, well-being, and peace of mind of those we serve.</h3><h3 class="jobSectionHeader">
Join our globally recognized brand, where trust, communication, and a positive culture are at the core of everything we do. Our leadership is consistent, approachable, and supportive-ensuring your well-being and work-life balance.</h3><h3 class="jobSectionHeader">
We're looking for individuals who thrive in collaborative environments, are passionate about meaningful change, and want to grow in a company that puts people first.</h3><h3 class="jobSectionHeader">
At Cigna, you'll be part of a purpose-driven team that values innovation, compassion, and impact. Whether you're shaping better care experiences or supporting customers through life's key moments, your work will matter.</h3><h3 class="jobSectionHeader">
Grow with us-and help shape the future of healthcare.</h3>
Purpose of this Role<ul><li>
The Lead AI Architect (AI) is responsible for defining, adopting, and assuring AI-enabled data architectures that are commercially viable, technically robust, and aligned to Cigna&rsquo;s enterprise and data strategy.</li><li>
As part of the International Health Global Architecture function, this role sets architectural direction and ensures strategic priorities are realised through data and AI delivery.</li><li>
This role will drive innovation, productivity and operational improvements, and customer experience transformation by applying modern AI architecture in a secure, scalable, and responsible manner.
</li><li>This role collaborates closely with business leaders, product teams, engineering, and third-party partners to ensure data and AI investments deliver measurable value while meeting regulatory, operational, and security requirements.</li></ul>
Main Duties / Responsibilities
Strategy &amp; Architecture Leadership<ul><li>
Architect and own enterprise-scale Generative AI platforms supporting multiple business use cases, including internal pilots, automation assistants, knowledge discovery, and customer-facing AI experiences.</li><li>
Define and maintain reference target-state data and AI architectures for International Markets, aligned to enterprise architecture standards and data strategy.</li><li>
Act as a senior architectural authority for AI-enabled data solutions, ensuring consistency, reuse, and long-term sustainability across the landscape.</li><li>
Advocate for AI-driven data design, embedding modern AI and analytics patterns into enterprise data platforms and services.</li><li>
Maintain an active awareness of emerging trends in data architecture, AI/ML, and enterprise technology, assessing their potential impact and value for Cigna, advising senior leadership on AI strategy, feasibility and adoption paths.</li></ul>
AI Architecture<ul><li>
Define architectural patterns and standards to support AI model lifecycle management, AI and data pipelines, feature engineering, and associated design artefacts.</li><li>
Architect and own enterprise-scale Generative AI platforms supporting multiple business use cases, including internal pilots, automation assistants, knowledge discovery, and customer-facing AI experiences.
</li><li>Define and maintain reference architectures and design patterns for LLM-based applications, covering model access, orchestration, retrieval, prompt management, evaluation, and observability.
</li><li>Ensure that AI architectures support the needs of data science, analytics, and AI use cases, including scalability, performance, data quality, and governance.</li><li>
Work with Solution Architects and engineering teams to translate business and technical requirements into coherent end-to-end data and AI solution designs.</li></ul>
Delivery &amp; Governance<ul><li>
Participate across the full delivery lifecycle, from early concept shaping and investment cases through to design assurance and governance during build and implementation.</li></ul><ul><li>
Provide architecture governance through design reviews, standards definition, and participation in architecture review and approval forums.</li><li>
Capture and manage architectural risks, issues, and assumptions, articulating their financial, operational, and delivery impacts.</li><li>
Support sponsors in the creation of well-rounded and compelling business cases for data and AI-led change.</li></ul>
Stakeholder &amp; Commercial Leadership<ul><li>
Proactively engage with stakeholders across Business, IT, and third-party partners to ensure solutions are cost-effective, appropriate, and aligned to business outcomes.</li><li>
Take a lead role in the selection and assessment of third-party data and AI solutions, establishing and maintaining effective supplier and partner relationships where required.</li><li>
Apply strong commercial awareness, including financial planning, budgeting, and cost-benefit considerations, in architectural decision-making.</li></ul>
Capability Building &amp; Mentoring<ul><li>
Provide high-level mentoring and guidance to architects, design, and development teams to embed data and AI principles, standards, and best practices.</li><li>
Take a lead role in the selection and assessment of third-party data and AI solutions, establishing and maintaining effective supplier and partner relationships where required.</li><li>
Contribute to the maturity of Cigna&rsquo;s data and AI architecture capability through knowledge sharing, standards development, and continuous improvement.</li><li>
Apply strong commercial awareness, including financial planning, budgeting, and cost-benefit considerations, in architectural decision-making.</li></ul>
Skills &amp; Experience
Essential Experience<ul><li>
Demonstrated expertise in architecting and leading large-scale AI initiatives, taking solutions from proof of concept through to successful production deployment.</li><li>
Deep understanding of architectural considerations and trade-offs in AI and machine learning projects, built through hands-on delivery experience.</li><li>
Experience working effectively within globally distributed teams and complex stakeholder environments.</li></ul>
Generative AI &amp; Core Concepts:<ul><li>
Large Language Models (LLMs), embeddings, prompt engineering - Retrieval-Augmented Generation (RAG), Knowledge bases and Multimodal embeddings</li><li>
Agentic AI workflows and tool calling - Model Context Protocol (MCP) concepts and context management</li><li>
Knowledge on foundational models and inference profiles, regional hosting and restrictions of various models and be able to identify the right model for the right requirements.</li></ul>
AI Architecture &amp; Tooling<ul><li>
Awareness of AI Gateway / model access layer design and access controls on the gateways.</li></ul><ul><li>
Proficiency in designing and implementing AI Gateway/model access layers to facilitate secure and scalable access to AI models.</li><li>
Experience in architecting solutions with vector databases and semantic search for efficient retrieval of information from large volumes of unstructured data.</li><li>
Skilled in leveraging document intelligence platforms (e.g., Azure Form Recognizer, AWS Textract, Google Document AI) for document classification, data extraction, and entity recognition from varied formats including scanned documents, PDFs, and handwritten forms.</li><li>
Knowledge of Intelligent Document Processing (IDP) solutions for automated ingestion, parsing, and validation of unstructured documents, ensuring accurate routing of documents to appropriate business workflows.</li><li>
Familiarity with workflow orchestration frameworks (such as LangChain, LangGraph, or equivalent) to automate end-to-end document processing pipelines, including document routing, exception handling, and integration with downstream systems.</li><li>
Understanding of optical character recognition (OCR), natural language processing (NLP) techniques, and entity linking for extracting actionable insights from diverse data types.</li><li>
Awareness of data validation, quality checks, and document lifecycle management best practices to ensure compliance and reliability in document processing solutions.</li></ul><ul><li>
End-to-end AI pipelines: ingestion, retrieval, generation, evaluation and rollout.</li><li>
Familiarity with AI governance, model lifecycle considerations, and responsible use of data and AI, particularly within regulated environments.</li></ul>
Qualifications<ul><li>
Advanced degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field.</li><li>
10+ years of experience building and architecting large-scale software or AI systems in medium-to-large enterprises.</li><li>
5+ years of experience with AI/ML, NLP, or Generative AI</li><li>
Experience working in regulated or compliance-sensitive environments (e.g., healthcare, finance, insurance, global enterprises).</li><li>
Strong communication skills with the ability to influence technical and non-technical stakeholders, with English at C2 level.</li></ul>
Why You'll Love Working here<ul><li>
Competitive salary</li><li>
Multicultural and hybrid working environment</li><li>
Private Medical Insurance</li><li>
Employee Wellbeing Benefits</li><li>
Educational Development Program</li></ul>
About Cigna Healthcare
Cigna Healthcare, a division of The Cigna Group, is an advocate for better health through every stage of life. We guide our customers through the health care system, empowering them with the information and insight they need to make the best choices for improving their health and vitality. Join us in driving growth and improving lives.
Qualified applicants will be considered without regard to race, color, age, disability, sex, childbirth (including pregnancy) or related medical conditions including but not limited to lactation, sexual orientation, gender identity or expression, veteran or military status, religion, national origin, ancestry, marital or familial status, genetic information, status with regard to public assistance, citizenship status or any other characteristic protected by applicable equal employment opportunity laws.
If you require reasonable accommodation in completing the online application process, please email: SeeYourselfEMEA@cigna.com for support. Do not email SeeYourselfEMEA@cigna.com for an update on your application or to provide your resume as you will not receive a response.</div>

Lead AI Architect

c2 labs

<div><h2 class="jobSectionHeader">Position Summary</h2>

C2 Labs is seeking a Principal AI Platform Architect to help design and deliver AI-powered solutions that improve operational efficiency, automation, and decision-making across regulated environments that is on site in Knoxville, TN.

This role will lead the architecture and implementation of enterprise AI capabilities, including intelligent automation, knowledge systems, workflow orchestration, and platform integrations, while ensuring scalability, security, and governance.
<h2 class="jobSectionHeader">
What You'll Do</h2>
<h3 class="jobSectionHeader">Security, Compliance &amp; Governance</h3>
<ul><li>Design secure AI architectures for regulated environments</li>
<li>Implement AI governance and model security controls</li>
<li>Support FedRAMP, RMF, CMMC, and government compliance requirements</li>
<li>Develop reusable compliance intelligence and automation frameworks</li>
</ul><h3 class="jobSectionHeader">AI Platform Architecture</h3>
<ul><li>Define and evolve enterprise AI platform strategy</li>
<li>Design scalable AI services, agent frameworks, and orchestration patterns</li>
<li>Evaluate and integrate emerging AI technologies</li>
<li>Establish AI governance, security, and architecture standards</li>
</ul><h3 class="jobSectionHeader">Agentic AI &amp; Knowledge Systems</h3>
<ul><li>Design multi-agent systems and human-in-the-loop workflows</li>
<li>Build enterprise copilots, digital workers, and AI-driven automation</li>
<li>Architect Retrieval-Augmented Generation (RAG) solutions</li>
<li>Design vector search, semantic retrieval, and knowledge architectures</li>
<li>Integrate structured and unstructured enterprise data sources</li>
</ul><h3 class="jobSectionHeader">Platform Integration &amp; Automation</h3>
<ul><li>Design API-first architectures and workflow orchestration strategies</li>
<li>Integrate AI capabilities with:
<ul><li>Microsoft Copilot Studio</li>
<li>Power Platform</li>
<li>ServiceNow</li>
<li>Archer</li>
<li>eMASS</li>
<li>Security and compliance tooling</li>
</ul></li></ul><h2 class="jobSectionHeader">Required Qualifications</h2>
<h3 class="jobSectionHeader">Experience</h3>
<ul><li>10+ years of software, platform, or solution architecture experience</li>
<li>3+ years building production AI systems</li>
<li>Experience leading architecture from concept through deployment</li>
</ul><h3 class="jobSectionHeader">Technical Expertise</h3>
<ul><li>Agentic AI architectures</li>
<li>RAG and vector databases</li>
<li>LLM orchestration frameworks</li>
<li>Cloud-native architectures</li>
<li>Enterprise SaaS platforms</li>
<li>API integrations and workflow orchestration</li>
<li>OpenAI, Anthropic, Azure AI, AWS Bedrock, or Vertex AI</li>
</ul><h3 class="jobSectionHeader">Preferred</h3>
<ul><li>Archer, RegScale, eMASS, Xacta, or similar platforms</li>
<li>Product strategy, enterprise architecture, or AI productization experience</li>
</ul><h2 class="jobSectionHeader">Why Join C2 Labs?</h2>

You'll have the opportunity to shape a first-of-its-kind AI-powered compliance platform that combines intelligent agents, enterprise knowledge systems, and automation to transform how organizations achieve and maintain compliance in highly regulated environments.

EEO STATEMENT:

We are an equal opportunity employer. All qualified applicants will be considered without discrimination based on race, color, religion, sex, national origin, age, disability, or protected veteran status. Employment offers will be contingent on passing a pre-employment drug screen.</div>

Principal AI Architect

The posted job opening may be for a Temporary Fixed Term period of time. If the selected applicant is hired as a Temporary Fixed Term employee/associate, their employment with Cognizant will typically be for a period of six to nine months. Any extensions to the temporary employee’s employment period will be solely at the discretion of Cognizant Technology Solutions. About the role
 As a Gen AI Architect , you will make an impact by designing and implementing end-to-end artificial intelligence and machine learning solutions that support safe, scalable, and compliant medical device products. You will be a valued member of the AI and data engineering team and work collaboratively with data scientists, engineers, and compliance stakeholders to deliver innovative solutions that meet regulatory and clinical standards. In this role, you will:
 Design end-to-end machine learning architectures using AWS AI/ML services and MLOps practices for scalable and secure medical device applications
 Develop reusable MLflow-based frameworks for experiment tracking, model versioning, and governance across projects
 Build automated ML pipelines using AWS CodePipeline and related services to enable CI/CD for machine learning models
 Collaborate with cross-functional teams to translate AI/ML concepts into production-ready solutions aligned with regulatory requirements
 Define standards for data ingestion, feature engineering, and model serving to meet performance, reliability, and compliance goals
 Optimize deployment of generative AI and traditional ML models on AWS, balancing cost, scalability, and compliance
 Implement monitoring strategies for model drift, data drift, and system performance using MLOps tools
 Document architectures, decisions, and operational processes to support audits and regulatory reviews
 Partner with compliance and quality teams to ensure traceability, validation, and risk management in medical device AI solutions
 Promote secure coding and data protection practices for sensitive healthcare data
 Review and enhance existing AI/ML solutions to improve scalability, reliability, and clinical safety
 Support modernization of legacy systems into AI-driven solutions for improved clinical outcomes
 Advise stakeholders on how AI and ML capabilities can enhance medical device offerings responsibly Work model
 We believe hybrid work is the way forward as we strive to provide flexibility wherever possible. Based on this role’s business requirements, this is a hybrid position requiring 3 days a week in a Cognizant office or client location in Chicago, IL. Regardless of your working arrangement, we are here to support a healthy work-life balance through our various wellbeing programs.
 The working arrangements for this role are accurate as of the date of posting. This may change based on the project you’re engaged in, as well as business and client requirements. Rest assured; we will always be clear about role expectations. What you need to have to be considered:
 9–12 years of experience in AI/ML engineering, solution architecture, or data science
 Strong hands-on experience with AWS machine learning services and cloud-native architecture design
 Proven expertise in MLOps practices, including MLflow, model lifecycle management, and governance
 Experience implementing CI/CD pipelines for ML using AWS CodePipeline or similar tools
 Deep understanding of machine learning concepts, including model development, evaluation, and feature engineering
 Hands-on experience with generative AI techniques and frameworks
 Experience working in regulated environments, preferably in medical devices or healthcare
 Strong understanding of data security, compliance, and privacy standards
 Ability to communicate effectively with both technical and non-technical stakeholders These will help you stand out:
 Experience delivering audit-ready, compliant AI/ML solutions in the medical device domain
 Familiarity with regulatory frameworks and validation processes for healthcare technologies
 Strong knowledge of responsible AI practices and risk mitigation strategies
 Experience designing highly scalable, secure cloud architectures for sensitive data
 Ability to bridge innovation with compliance in complex enterprise environments Salary and Other Compensation:
 Applications will be accepted until July 2, 2026 .
 The hourly salary for this position is between $88/hr - $100/hr depending on experience and other qualifications of the successful candidate.
 This position is also eligible for Cognizant’s discretionary annual incentive program, based on performance and subject to the terms of Cognizant’s applicable plans.
 Benefits: Cognizant offers the following benefits for this position, subject to applicable eligibility requirements:
 
Medical/Dental/Life Insurance
 
Long-term/Short-term Disability
 Employee Stock Purchase Plan
 Disclaimer: The salary, other compensation, and benefits information is accurate as of the date of this posting. Cognizant reserves the right to modify this information at any time, subject to applicable law.

Gen AI Architect

Braze

<div><div>At Braze, we have found our people. We're a genuinely approachable, exceptionally kind, and intensely passionate crew.

We seek to ignite that passion by setting high standards, championing teamwork, and creating work-life harmony as we collectively navigate rapid growth on a global scale while striving for greater equity and opportunity &ndash; inside and outside our organization.

To flourish here, you must be prepared to set a high bar for yourself and those around you. There is always a way to contribute: Acting with autonomy, having accountability and being open to new perspectives are essential to our continued success.

Our deep curiosity to learn and our eagerness to share diverse passions with others gives us balance and injects a one-of-a-kind vibrancy into our culture.

If you are driven to solve exhilarating challenges and have a bias toward action in the face of change, you will be empowered to make a real impact here, with a sharp and passionate team at your back. If Braze sounds like a place where you can thrive, we can't wait to meet you.</div>
Braze is building an Applied AI function for its GTM organization to help pioneer how modern revenue teams operate with AI. This is a chance to work at the frontier of AI-enabled GTM execution at a scaled global SaaS company with sophisticated enterprise customers, advanced buyers, and a large customer-facing organization.

The team encodes the judgment of its best practitioners into systems that extend it across the entire revenue org, creates feedback loops that surface where execution can improve, and builds the AI-powered systems that make every GTM team member more effective at the moments that matter most.

This team is field-attached, product-minded, and outcome-driven. We treat GTM practitioners as users and their workflows as product surfaces. We build agents, intelligence layers, automations, and decision-support tools, then tune and refine them based on adoption, output quality, field feedback, and business impact. The goal is practical AI that earns trust in live GTM workflows and creates durable value at scale.
<h2 class="jobSectionHeader">
WHAT YOU'LL DO</h2>

As an Applied AI Architect, you will own a defined surface area across the revenue lifecycle and use AI to transform how Braze executes within it. You'll operate across three modes simultaneously: as the practitioner whose field judgment (in part) the agents in your domain are modeled after, as the builder who designs and refines those agents on top of shared infrastructure, and as the observer who determines whether the outputs are actually changing how GTM teams engage with customers and prospects.
 
 
Your success is measured by whether practitioners adopt the outputs, whether those outputs improve behavior at critical moments, and whether that behavioral change produces measurable revenue and customer impact.
<ul>
<li>Conduct stakeholder research across the GTM motion, from pre-sales through post-sales, to map where intelligence gaps, workflow friction, and handoff failures are most acute. Translate findings into a structured and prioritized backlog of problems to solve, including problem statements, impact, and feasibility scoring, dependencies, and stakeholders.</li>
<li>Build and refine AI agents tailored to your area of the revenue lifecycle, contributing directly to system architecture, retrieval logic, and output calibration on top of shared infrastructure. Ship working solutions against real workflows</li>
<li>Stay embedded in regional operating rhythms: pipeline reviews, deal cycles, QBRs, renewal planning, and account strategy sessions. The system gets better because the individuals building it never leave the commercial and customer motion. Field proximity is the operating discipline.</li>
<li>Own the reliability and quality of the systems you manage. Monitor adoption, diagnose output failures, and tune/iterate continuously. You are not handing off to an ops team. You operate what you ship.</li>
<li>Contribute to the shared knowledge hub by validating field signals, structuring deals and customer patterns, and ensuring that intelligence captured across the revenue lifecycle flows back in a form that produces better agent outputs.</li>
<li>Recognize patterns across your area and scale what works. A system built for one account scenario, customer segment, or deal stage should become reusable infrastructure for adjacent situations.</li>
<li>Partner with Growth Engineering, PMM, Solutions Consulting, Pricing, Customer Success, and leadership to ensure the systems you build are grounded in a cross-functional context and adopted by the people they serve.</li>
<li>Help shape what this function becomes as the technology and the role category mature. The playbook is being written in real time, and you will be one of the authors.</li>
</ul><h2 class="jobSectionHeader">WHO YOU ARE</h2>

We're looking for people who have lived the revenue lifecycle and can translate that experience into AI systems that scale practitioner judgment beyond what any individual can. You are highly autonomous, technically minded, self-directed, and comfortable operating as a senior individual contributor in a new domain.
<ul>
<li>You are a builder. You get excited by taking ideas from concept to reality. You have used AI to build agents, automations, or tools that changed how real work gets done. You can scope and prioritize solutions, contribute to system architecture and retrieval design, evaluate outputs for quality, and diagnose why something was missed at the system level rather than just the content level. You own what you help build, and you improve it based on what you observe.</li>
<li>You carry GTM judgement. You have spent a meaningful amount of time in a revenue role. You know what a discovery call that misses the real objection sounds like. You understand how deals move, where they stall, how buying committees make decisions, and what makes the difference between a customer who renews and expands versus one who churns. You understand the daily reality of AEs, SCs, BDRs, sales directors, CSMs, and account managers, because you have either done their job or worked closely alongside them for years.</li>
<li>You think like a product leader. When you identify a gap in the field, you scope the problem, define the user, map the workflow, and build the solution. You think in terms of adoption. You know the difference between shipping something and shipping something people actually use. You treat GTM practitioners as your users and their workflows as your product surface.</li>
<li>You are a pattern recognizer and systems thinker. When you build something that works for one scenario, you immediately see how it applies across adjacent workflows, customer segments, or deal stages. You think in reusable components and scalable frameworks.</li>
<li>You are field-attached by instinct. You would rather be embedded in pipeline reviews, deal cycles, and customer strategy sessions than building from a desk. You know that the quality of what you build depends on staying close enough to the revenue motion to understand whether it is actually useful.</li>
<li>You are playing a long game. GTM is being rebuilt around AI, and the people who understand both the field and the technology will define what that looks like. This role is an opportunity to be on that team early, to develop the skills and track record that will matter most over the next decade, and to do it inside a company with the scale and sophistication to make the work real. You want to build something that lasts.</li>
</ul><h2 class="jobSectionHeader">Minimum requirements</h2>
<ul><li>6+ years of professional experience in a GTM role with direct involvement in the commercial motion: Solutions Consulting, Solutions Architecture, Account Executive, Sales Leader, GTM strategy, or a comparable role at a B2B SaaS company</li>
<li>Demonstrated, hands-on experience building AI-powered tools, agents, automations, or workflows that transformed real work processes, with concrete examples you can speak to in depth</li>
<li>Technical fluency sufficient to engage credibly on integration architecture, evaluate agent outputs at the system level, and contribute directly to prompt design, retrieval logic, and data workflows (e.g., Python, APIs, integrations) without always requiring translation from an engineering counterpart</li>
<li>Strong understanding of enterprise sales workflows, deal stages, and the daily operating reality of the GTM personas you will be building for: AEs, SCs, BDRs, sales leadership, CS, and business value teams</li>
<li>Strong field credibility with experience in executive-level customer engagement, sufficient to walk into an opportunity or executive business review and have the room take the output seriously</li>
<li>A track record of taking solutions from problem identification through field adoption, not just building and handing off</li>
<li>Comfort working across multiple workstreams simultaneously, balancing field engagement with system build and continuous refinement</li>
<li>Highly autonomous, self-directed, and comfortable operating as a senior individual contributor</li>
</ul><h2 class="jobSectionHeader">Preferred qualifications</h2>
<ul><li>Product Management background or experience, either as a career transition into GTM or as a complement to a field role, with demonstrated ability to ship things people actually use</li>
<li>Experience in Solutions Consulting, Solutions Engineering, or field architecture roles within enterprise SaaS</li>
<li>Proficiency with AI development tools and platforms (e.g., Claude, Claude Code, Cursor, custom agent frameworks, API integrations, workflow automation tools)</li>
<li>Familiarity with enterprise SaaS technology stacks (e.g., Salesforce, Gong, Slack, Snowflake)</li>
<li>Experience building and scaling internal tools or systems that were adopted beyond your immediate team</li>
<li>Analytical capability sufficient to evaluate deal data, diagnose output failures, and build data-driven recommendation systems</li>
</ul><h2 class="jobSectionHeader">Why this role matters</h2>

The best GTM practitioners, whether they are closing new business, managing renewals, or driving expansion, operate partly on instincts and context that others don't always have access to. Those instincts are built over years of field exposure, thousands of customer interactions, and pattern recognition that compounds with experience.
 
 
The technology now exists to encode practitioner judgment into systems that deliver it at scale. But building those systems requires people who carry the judgment in the first place. An engineer without field depth builds a technically correct system that the field ignores. A field practitioner without built capability creates insight that doesn't scale. The person who is both is in a unique position, and that intersection is where this role sits.

Braze operates at a global scale, with sophisticated enterprise customers, advanced buyers, and a large customer-facing organization. The patterns being built here will become the standard across SaaS. The window to be part of building them from the ground up is open right now.

If you have lived in the commercial motion, already built with AI, and want to define the frontier of what this function looks like at a scaled enterprise company, we would like to talk.

For candidates based in the United States, the pay range for this position at the start of employment is expected to be between $123,000 and $184,000/year, with an expected On Target Earnings (OTE) between $145,000 and $217,000/year (including bonus or commission). Your exact offer may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. In addition to cash compensation, this role qualifies for a comprehensive Total Rewards package that includes equity grants of restricted stock (RSUs) so that you will own a piece of our company.

#LI-Hybrid<div><div>

WHAT WE OFFER 

Braze benefits vary by location, and we encourage you to review our specific benefits offerings for each country here. More details on benefits plans will be provided if you receive an offer of employment.

From offering comprehensive benefits to fostering hybrid ways of working, we've got you covered so you can prioritize work-life harmony. Braze offers benefits such as:
<ul>
<li>Competitive compensation that may include equity</li>
<li>Retirement and Employee Stock Purchase Plans</li>
<li>Flexible paid time off</li>
<li>Comprehensive benefit plans covering medical, dental, vision, life, and disability</li>
<li>Family services that include fertility benefits and equal paid parental leave</li>
<li>Professional development supported by formal career pathing, learning platforms, and a yearly learning stipend</li>
<li>A curated in-office employee experience, designed to foster community, team connections, and innovation</li>
<li>Opportunities to give back to your community, including an annual company-wide Volunteer Week and donation matching</li>
<li>Employee Resource Groups that provide supportive communities within Braze</li>
<li>Collaborative, transparent, and fun culture recognized as a Great Place to Work&reg;</li>
</ul>
ABOUT BRAZE 
 
Braze is the leading customer engagement platform that empowers brands to Be Absolutely Engaging&trade;. Braze helps brands deliver great customer experiences that drive value both for consumers and for their businesses. Built on a foundation of composable intelligence, BrazeAI&trade; allows marketers to combine and activate AI agents, models, and features at every touchpoint throughout the Braze Customer Engagement Platform for smarter, faster, and more meaningful customer engagement. From cross-channel messaging and journey orchestration to Al-powered decisioning and optimization, Braze enables companies to turn action into interaction through autonomous, 1:1 personalized experiences.

The company has been consistently recognized as a Leader in marketing technology by industry analysts, and was named a G2 &quot;Best of Marketing and Digital Advertising Software Product&quot; in 2026. Braze was also named a 2026 Best Places to Work by Built In, a 2025 America's Greenest Companies by Newsweek, and a 2025 Fortune Best Workplace in Technology&trade; by Great Place To Work&reg;. Braze is also proudly certified as a Great Place to Work&reg; in the U.S., the UK, Australia, and Singapore.

The company is headquartered in New York with offices in Austin, Berlin, Bucharest, Chicago, Dubai, Jakarta, London, Paris, San Francisco, S&atilde;o Paulo, Singapore, Seoul, Sydney and Tokyo.

BRAZE IS AN EQUAL OPPORTUNITY EMPLOYER

At Braze, we strive to create equitable growth and opportunities inside and outside the organization.

Building meaningful connections is at the heart of everything we do, and that includes our recruiting practices. We're committed to offering all candidates a fair, accessible, and inclusive experience &ndash; regardless of age, color, disability, gender identity, marital status, maternity, national origin, pregnancy, race, religion, sex, sexual orientation, or status as a protected veteran. When applying and interviewing with Braze, we want you to feel comfortable showcasing what makes you you.

We know that sometimes different circumstances can lead talented people to hesitate to apply for a role unless they meet 100% of the criteria. If this sounds familiar, we encourage you to apply, as we'd love to meet you. 
 
OUR AI-POWERED BRAZE RECRUITMENT PROCESS 
 
At Braze, we're committed to a fair and transparent candidate experience. To help our recruitment teams focus on what matters most &mdash; the person behind each application &mdash; we use AI-assisted tools at certain stages of our recruitment process.
</div>
<div>This includes using AI to analyze the experience, skills and qualifications in your application materials to help with screening and prioritizing candidates. Such screening may amount to a form of solely automated decision-making. We also use AI for administrative support, like scheduling and recording interviews and summarizing interview notes. Our recruiting teams remain responsible for all hiring decisions and are involved throughout the process.

Depending on where you are located, you may have the right to request further information about how AI is used in our recruitment process, to opt out of AI-assisted review, to request a manual review of any decision made or to contest a decision.

Please contact us at talentdata.privacy@braze.com for any requests or questions. To find out more about our hiring process, check out this page.

Notice Regarding Automated Employment Decision Tool (NYC Local Law 144)

Our use of AI during the application review process may include the use of automated employment decision tools. Pursuant to New York City Local Law 144, for roles based in New York City, or if you reside in New York City, you have the right to request an alternative selection process or a reasonable accommodation instead of AI-assisted review. Please submit any such request to our Talent Acquisition team at talentdata.privacy@braze.com promptly after applying. A summary of the most recent bias audit results for such tool is available here. 
 
Please see our Candidate Privacy Policy for more information on how Braze processes your personal information during the recruitment process and, if applicable based on your location, how you can exercise any privacy rights.</div></div></div>

Applied AI Architect

Contango

<div>Contract duration: 6 months (with a potential extension) 
Engagement Type: Full-time 
Start date: July 2026 
Location: Abu Dhabi (on-site)
Role Overview
We are seeking an experienced AI Architect to define how we design, secure, and scale AI across the organization. You&rsquo;ll translate business strategy into reference architectures, platform decisions, and guardrails that engineering teams execute against &mdash; balancing capability, cost, risk, and time-to-value.
Where AI Engineers ship features, you&rsquo;ll shape the platform: model strategy, the data and retrieval foundation, the LLMOps backbone, the security posture, and the governance model. Increasingly, that means architecting agentic systems, multi-model routing, and responsible AI controls that comply with regulations like the EU AI Act. 
Key Responsibilities<ul><li>
Define AI strategy and reference architectures for generative AI, agentic systems, RAG, and predictive ML.</li><li>
Set model strategy: build-vs-buy, open-weight vs. hosted, multi-model routing, and a framework for evaluating new models as they ship.</li><li>
Architect the data and retrieval foundation &mdash; lakehouse, feature stores, vector stores, and lineage &mdash; that AI systems depend on.</li><li>
Design the LLMOps/MLOps platform: experiment tracking, model and prompt registries, automated eval gates, and observability.</li><li>
Lead AI security: threat modeling for prompt injection and data exfiltration, and OWASP LLM Top 10 mitigations.</li><li>
Own responsible AI and governance &mdash; risk classification, human oversight, auditability, and alignment with the EU AI Act, NIST AI RMF, and ISO/IEC 42001.</li><li>
Establish FinOps for AI: cost models, token budgeting, and unit economics per use case.</li><li>
Provide technical leadership, review designs, mentor engineers, and align stakeholders from engineering to the executive team. 
 
Candidate Requirements</li></ul><ul><li>
Demonstrated experience delivering AI solutions in an enterprise or client-facing/consulting environment.</li><li>
Prior experience at management consulting firms and/or Big Tech is an advantage.</li><li>
Bachelor's or Master&rsquo;s degree in Computer Science, Engineering, Information Technology, or a related field.</li><li>
8+ years in software/data engineering, with 3+ years architecting AI/ML or large-scale data systems.</li><li>
A track record of designing and delivering production AI systems end-to-end, ideally including GenAI/LLM workloads.</li><li>
Deep understanding of LLM and ML system design &mdash; RAG, agents, fine-tuning trade-offs, evaluation, serving, and the full MLOps/LLMOps lifecycle.</li><li>
Strong cloud architecture skills (Azure, AWS, or GCP) plus a modern data platform (Databricks or Snowflake).</li><li>
Expertise in AI security, responsible AI, and data governance, with awareness of relevant regulations.</li><li>
Excellent communication &mdash; able to influence both engineers and executives.</li><li>
Hands-on enough to prototype, review code, and earn engineering teams&rsquo; trust. 
Nice to have:
</li><li>Architecting AI for regulated or sovereign environments (finance, healthcare, government, GCC/UAE).</li><li>
Enterprise architecture frameworks (TOGAF) and well-architected reviews.</li><li>
Defining golden-path tooling for internal AI development.</li><li>
Running structured vendor PoCs and bake-offs.</li></ul>
Tools &amp; Technologies You&rsquo;ll Work With:<ul><li>
Patterns: RAG, agentic/multi-agent, event-driven, lakehouse/medallion; reusable blueprints.</li><li>
Model strategy: multi-model routing and gateways (LiteLLM, Portkey); Claude, GPT, Gemini, Llama, Mistral.</li><li>
Platforms: Azure AI Foundry, AWS Bedrock, Vertex AI, Databricks Mosaic AI.</li><li>
Data foundation: Delta Lake/Databricks, Snowflake, Unity Catalog, vector stores, knowledge graphs.</li><li>
LLMOps/MLOps: MLflow, LangSmith/Langfuse, model and prompt registries, eval-in-CI.</li><li>
Security &amp; governance: OWASP LLM Top 10, NIST AI RMF, ISO/IEC 42001, EU AI Act, RBAC/ABAC, secrets management.</li><li>
Infra &amp; observability: Kubernetes, Terraform/OpenTofu, OpenTelemetry; token/cost dashboards.</li></ul></div>

AI Architect (6 months contract)

EPAM Systems

<div>We're looking for a Senior AI Architect to join our team in Germany in a hybrid working mode. In this role, you will help clients design and scale production-grade AI platforms and solutions on Microsoft Azure. You will work at the intersection of enterprise architecture, cloud-native engineering, GitHub-enabled software delivery and responsible AI adoption.
This position focuses on shaping large-scale AI transformation programs that connect business outcomes with practical architecture design. You will engage in areas such as Azure AI platforms, copilots, RAG, agentic systems, governance, observability, evaluation, security and Responsible AI practices.
Unlike roles centered on proofs of concept, this opportunity involves delivering enterprise-grade AI systems embedded into real workflows, including engineering productivity, knowledge retrieval, automation and AI-enabled delivery. The challenges go beyond basic API calls and span AI-native software engineering, GitHub Copilot integration, secure architectures, FinOps and governance at scale.
You will collaborate with architects, engineers, Microsoft/GitHub specialists and client leadership teams to drive secure and governed AI adoption.
Responsibilities<ul><li>
Define target-state architectures for AI platforms, assistants, RAG systems, agents, AI gateways and AI-native engineering workflows
</li><li>Lead architecture discovery, maturity assessments, technical roadmaps, platform decisions and delivery governance
</li><li>Design Azure-first architectures using Microsoft Foundry, Azure OpenAI, Azure AI Search, Azure API Management, AKS, Azure Functions, Application Insights, Azure Monitor, Key Vault, Microsoft Entra ID, Cosmos DB, PostgreSQL and Microsoft Fabric where applicable</li><li>
Design AI-native engineering systems using GitHub Copilot, GitHub Enterprise, GitHub Advanced Security, agent-ready repositories, secure pull-request practices and AI-assisted delivery patterns
</li><li>Design harnesses around AI systems, including agent instructions, tool contracts, MCP integrations, retrieval grounding, evaluation suites, telemetry, cost controls, policy controls and human approval gates
</li><li>Define governance and operational frameworks for Responsible AI, security, identity, FinOps and observability
</li><li>Translate business outcomes into target architecture, executive narratives, platform decisions and delivery governance across client leadership, architecture, engineering and security teams
</li><li>Facilitate workshops, hackathons and enablement sessions focused on AI adoption, Copilot integration and engineering excellence
</li><li>Convert practical experience into reusable patterns, accelerators and architectural blueprints
</li><li>Mentor architects and engineers and contribute to presales and solution design initiatives
</li></ul>Requirements<ul><li>
10+ years in solution architecture, cloud platforms or platform engineering
</li><li>Strong experience with enterprise AI, GenAI, agentic systems, RAG and AI-native engineering
</li><li>Proficiency with Microsoft Azure and modern cloud-native architecture
</li><li>Knowledge of GitHub workflows (CI/CD, DevOps, IaC) and secure engineering practices
</li><li>Track record of moving AI initiatives beyond PoC to enterprise-grade production environments
</li><li>Hands-on capability to validate prototypes, review code and guide engineering decisions
</li><li>Ability to explain how to make AI useful in production, not just impressive in a demo
</li><li>Capability to design platform architecture, governance models, adoption plans and AI delivery workflows
</li><li>Confidence working across enterprise realities, leveraging Azure and GitHub pragmatically
</li><li>Strong communication skills with both executives and technical stakeholders
</li></ul>Nice to have<ul><li>
Experience with GitHub Copilot Enterprise adoption, AI-native SDLC transformation, GitHub Actions and GitHub Advanced Security
</li><li>Familiarity with agent frameworks such as Semantic Kernel, LangChain, LangGraph, AutoGen, LlamaIndex, CrewAI or similar
</li><li>Knowledge of Microsoft 365 Copilot, Copilot Studio, Microsoft Graph, Teams, SharePoint or Power Platform integration
</li><li>Understanding of AI gateways, model routing, semantic caching, model abstractions, usage telemetry and AI FinOps
</li><li>Background in enterprise infrastructure with Terraform/IaC, Kubernetes/AKS, CI/CD and secure API gateway practices
</li><li>Awareness of agent reliability and security techniques, including workflow state, recovery, tool-permission boundaries, evaluation gates, prompt-injection defense, audit trails, rollback mechanisms and human approval workflows
</li><li>Familiarity with compliance and governance standards such as GDPR, EU AI Act readiness or ISO/IEC 42001
</li><li>Experience in regulated industries such as finance, healthcare, energy, retail, manufacturing or public sector
</li><li>Knowledge of other AI ecosystems, including AWS Bedrock, Google Vertex AI, Databricks, Snowflake, Anthropic, OpenAI API or Hugging Face</li></ul>
We offer<ul><li>
30 days holiday per annum</li><li>
Company Pension Scheme</li><li>
Regular performance assessments</li><li>
Discount on Fitness-First Black Membership</li><li>
bitkom - Corporate Benefits</li><li>
Employee Stock Purchase Plan (ESPP) (subject to certain eligibility requirements)</li><li>
Learning and development opportunities, including in-house training and coaching, professional certifications, and courses</li><li>
Friendly and enjoyable working team</li><li>
Regular corporate and social events</li><li>
Flexible and remote working opportunities</li><li>
Award-winning workplace: Great Place To Work&reg; certified in 2026, Kununu (Top Company 2022&ndash;2026), NewWork Business Award 2025 for outstanding culture, innovation and employee satisfaction.</li></ul></div>

Senior AI Architect - Microsoft Technologies

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