ai systems architect

MVP Application and game design L.L.C

We are looking for an Agentic AI Developer who can design, build, and optimize advanced AI systems that go beyond simple chatbot or basic RAG implementations. The ideal candidate should have hands-on experience creating multi-agent workflows, tool-using AI agents, retrieval systems, reasoning pipelines, orchestration layers, and production-ready AI architectures.This role requires someone who understands how to build complex AI systems where multiple components work together, including LLMs, vector databases, APIs, memory, tools, workflows, permissions, guardrails, and backend services. The candidate should be able to think architecturally, break down business problems into AI workflows, and design systems that are scalable, secure, reliable, and suitable for enterprise or government environments.The candidate should also have basic to intermediate knowledge of Node.js, enough to connect AI services with backend APIs, build lightweight services, integrate third-party tools, and support implementation with the engineering team.Technical RequirementsThe candidate should have hands-on experience with:<ul><li>Agentic AI frameworks such as LangGraph, LangChain, CrewAI, AutoGen, Semantic Kernel, or custom orchestration layers</li><li>Agent loops, planning/execution flows, task decomposition, reflection, validation, and human-in-the-loop workflows</li><li>Tool calling / function calling, structured outputs, JSON schemas, API execution, and tool safety controls</li><li>Stateful AI workflows, including state machines, graph-based orchestration, session state, and workflow persistence</li><li>Advanced RAG pipelines, including:</li><li>Chunking strategies</li><li>Embedding model selection</li><li>Hybrid search</li><li>Metadata filtering</li><li>Query rewriting</li><li>Reranking</li><li>Context compression</li><li>Retrieval evaluation</li><li>Hallucination mitigation</li><li>Memory architecture, including:</li><li>Short-term memory</li><li>Long-term memory</li><li>User-specific memory</li><li>Vector memory</li><li>Persistent knowledge stores</li><li>LLM guardrails, including:</li><li>Prompt injection protection</li><li>Permission-aware retrieval</li><li>Output validation</li><li>Policy checks</li><li>Tool execution safety</li><li>Response verification</li><li>LLM observability and evaluation, including:</li><li>Tracing</li><li>Prompt/version management</li><li>Eval datasets</li><li>Regression testing</li><li>Latency analysis</li><li>Cost monitoring</li><li>Failure analysis</li><li>Enterprise integrations, including REST APIs, databases, CRMs/ERPs, document stores, webhooks, queues, and workflow engines</li><li>Vector databases such as Qdrant, Weaviate, Pinecone, Milvus, Chroma, pgvector, Elasticsearch, or OpenSearch</li><li>Secure deployment architectures for private cloud, on-premise, offline, or government-secure environments</li><li>Node.js basics, including API development, async workflows, service integration, and connecting AI systems to backend applications</li></ul>Important NoteThis role is not for someone who has only built a basic chatbot or RAG over a few PDFs.We are looking for someone who can architect real agentic AI systems with:<ul><li>Agents</li><li>Tools</li><li>Memory</li><li>Retrieval</li><li>Validation</li><li>Orchestration</li><li>Security</li><li>Monitoring</li><li>Production deployment</li></ul>Work location:Abu Dhabi - in officeJob Type: Full-timePay: AED10,000.00 - AED11,000.00 per monthApplication Question(s):<ul><li>What is the difference between a basic RAG chatbot and an agentic AI system?</li><li>⁠How would you handle user permissions when the agent retrieves documents from multiple departments?</li><li>How would you evaluate and audit an agent&rsquo;s decisions, tool calls, and retrieved sources in production?</li></ul>Experience:<ul><li>AI Agent &amp; Routing Engineer: 2 years (Required)</li></ul>Language:<ul><li>English (Required)</li></ul>Work Location: In person

AI Systems Architect & Developer

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

Stradit

<div>We are seeking an experienced AI Engineering Lead to design, build, and scale advanced AI systems from model development through production deployment. The role involves leading the architecture of machine learning solutions, defining engineering best practices, and ensuring robust, reliable, and scalable AI workflows.
The ideal candidate has strong expertise in machine learning systems, data pipelines, and distributed computing, along with hands-on experience in Python and frameworks such as PyTorch, TensorFlow, or JAX. This role requires experience with cloud platforms (AWS, GCP, or Azure), containerization tools like Docker and Kubernetes, and familiarity with MLOps practices including model versioning, deployment, and monitoring.
You will collaborate closely with research, product, and engineering teams to deliver high-impact AI solutions, while also mentoring engineers and driving technical excellence across the organization. Strong leadership and communication skills are essential, along with a proven ability to bridge research and production environments.
This is a hybrid position for candidates located in NY/NJ that can commute to the office 4 days a week.
Required Qualifications<ul><li>
Lead the architecture and implementation of AI systems, from model development to deployment.</li><li>
Define engineering best practices for AI workflows, model lifecycle, and system reliability.</li><li>
8+ years of experience in software engineering, including 2+ years leading or mentoring teams.</li><li>
Strong background in machine learning systems, data pipelines, or distributed computing.</li><li>
Proficiency in Python (preferred), plus experience with frameworks such as PyTorch, TensorFlow, or JAX.</li><li>
Experience with cloud platforms (AWS, GCP, or Azure) and container orchestration (Kubernetes, Docker).</li><li>
Familiarity with MLOps tools (MLflow, Kubeflow, Airflow, etc.).</li><li>
Strong understanding of model deployment, versioning, and monitoring.</li><li>
Excellent communication skills and ability to collaborate with research and product stakeholders.</li></ul></div>

AI Tech Lead

Jerry

<div><div><div>Why Jerry.ai?</div><ul><li><div>
We are building the first super app to manage car ownership&mdash;an industry where the experience is stuck in the 90s.</div></li><li><div>
Lead a strategic area leveraging LLMs, agents, and internal APIs to automate a fragmented, $2T market.</div></li><li><div>
Work with partners at OpenAI to integrate Jerry.ai services directly into the ChatGPT App and pioneer new model capabilities.</div><div> 
</div></li></ul>
Lead the AI Revolution for Jerry.ai:
At Jerry.ai, we are moving past fragmented, time-consuming processes to create a seamless, automated platform.
You will sit at the intersection of product, engineering, and applied AI as you build and scale a sophisticated system that already automates &gt;70% of inbound sales and service requests (over 50k chats per month). Your role will include shaping how modern LLM systems, human-in-the-loop feedback, and computer-use agents redefine an entire industry.<div> 
</div>
How You Will Make an Impact:<ul><li><div>
Lead end-to-end development for our AI platform and customer experiences, from initial roadmap to rollout.</div></li><li><div>
Partner with engineers to design prompt strategies, evaluation frameworks, and guardrails&mdash;balancing latency, cost, and accuracy.</div></li><li><div>
Serve as the technical translator between engineering and the broader organization, establishing AI best practices and platform standards.</div></li><li><div>
Drive systematic improvement in answer quality, customer satisfaction, and automation rates through rigorous experimentation.</div></li><li><div>
Work with partners at OpenAI to evaluate and deploy the next generation of voice models and workflow automation.</div><div> 
</div></li></ul>
What We&rsquo;re Looking For in a Builder:<ul><li><div>
Intrinsically Motivated Technologist: You live and breathe AI. You read the release notes when a new model drops and you&rsquo;ve already built your own custom workflows.</div></li><li><div>
Systems Thinker: You are comfortable diving into technical conversations about API design and system architecture, translating complex concepts for any audience.</div></li><li><div>
Optimistic Problem-Solver: You are a &quot;how can we&quot; thinker who seeks constant improvement and thrives on owning high-impact metrics.</div></li><li><div>
Data-Driven with Conviction: You are familiar with SQL and comfortable diving into the data to answer your own questions and validate hypotheses.</div></li></ul><div> 
</div>
Ideal Profile:<ul><li><div>
4+ years of experience in management consulting or technical product management at a fast-paced startup.</div></li><li><div>
Proven interest in modern LLM systems.</div></li><li><div>
Ability to navigate technical tradeoffs and lead by example when implementing AI platform standards.</div></li><li><div>
A track record of taking complex, strategic ideas and turning them into scalable, production-grade products.</div></li></ul>
While we appreciate your interest and application, only applicants under consideration will be contacted.
Jerry.ai is proud to be an Equal Employment Opportunity employer. We prohibit discrimination based on race, religion, color, national origin, sex, pregnancy, reproductive health decisions or related medical conditions, sexual orientation, gender identity, gender expression, age, veteran status, disability, genetic information, or other characteristics protected by applicable local, state or federal laws.
Jerry.ai is committed to providing reasonable accommodations for individuals with disabilities in our job application process. If you need assistance or an accommodation due to a disability, please contact us at recruiting@jerry.ai
The successful candidate&rsquo;s starting pay will fall within the pay range listed on this job posting, determined based on job-related factors including, but not limited to, skills, experience, qualifications, work location, and market conditions. Ranges are market-dependent and may be modified in the future. In addition to base salary, the compensation may include opportunities for equity grants.
We offer a comprehensive benefits package to regular employees, including health, dental, and vision coverage, paid time off, paid parental leave, 401(K) plan with employer matching, and wellness benefits, among others. Equity opportunities may also be part of your total rewards package. Part-time, contract, or freelance roles may not be eligible for certain benefits.
About Jerry.ai
Jerry.ai is America&rsquo;s first and only super app to radically simplify car ownership. We are redefining how people manage owning a car, one of their most expensive and time-consuming assets.
Backed by artificial intelligence and machine learning, Jerry.ai simplifies and automates owning and maintaining a car while providing personalized services for all car owners' needs. We spend every day innovating and improving our AI-powered app to provide the best possible experience for our customers. From car insurance and financing to maintenance and safety, Jerry.ai does it all.
We are the #1 rated and most downloaded app in our category with a 4.7 star rating in the App Store. We have more than 5 million customers &mdash; and we&rsquo;re just getting started.
Jerry.ai was founded in 2017 by serial entrepreneurs and has raised more than $240 million in financing.
Join our team and work with passionate, curious and egoless people who love solving real-world problems. Help us build a revolutionary product that&rsquo;s disrupting a massive market.</div></div>

Senior Technical Product Manager, AI Engineering & Systems

Recutify Inc.

<div>Title : AI ML Tech Lead / AI Architect
Location Wilmington, DE 
Open FTCVisa Any 
Day 1 onsite 5 Days 
<div> 
Key Responsibilities</div>
Architecture Ownership
<ul><li>Own the end to end architecture for the AI agent, DSL, and SFMC automation ecosystem.</li>
<li>Design agentic AI systems, backend microservices, APIs, and SFMC integrations (REST/SOAP).</li>
<li>Define DSL schemas (JSON/YAML) for AI generated workflows, ensuring extensibility, safety, and deterministic execution.</li>
<li>Establish guardrails, validation, simulation, and compliance frameworks for AI generated journeys and campaigns.</li>
<li>Create and maintain system blueprints, including data flow diagrams, integration contracts, and deployment architecture.</li>
</ul>Technical Leadership
<ul><li>Act as the hands on technical lead, guiding AI/ML engineers, DSL engineers, backend developers, and SFMC specialists.</li>
<li>Lead POCs, prototypes, and architectural spikes to validate design decisions and technology choices.</li>
<li>Drive coding standards, design patterns, and best practices across engineering teams.</li>
<li>Conduct architectural reviews, code reviews, and design walkthroughs.</li>
<li>Unblock teams, make technical decisions, and ensure alignment with architectural vision.</li>
</ul>AI/ML &amp; Agentic Systems
<ul><li>Partner with AI/ML teams on:
<ul><li>Agent frameworks (Agent SDK, LangChain, LangGraph, CrewAI, Semantic Kernel)</li>
<li>RAG pipelines, embeddings, and vectorization</li>
<li>LLM fine tuning, evaluation, and safety mechanisms</li>
</ul></li><li>Define prompting strategies, context engineering, and model interaction patterns.</li>
</ul>Backend, Cloud &amp; Integration Architecture
<ul><li>Architect cloud native, highly available systems on AWS using IaC (Terraform).</li>
<li>Oversee backend microservices, orchestration layers, and execution pipelines.</li>
<li>Ensure robust integration with SFMC components:
<ul><li>Journey Builder</li>
<li>Email Studio</li>
<li>Data Extensions</li>
<li>Personalization logic</li>
<li>REST/SOAP APIs</li>
</ul></li><li>Ensure observability, monitoring, logging, and reliability across all services.</li>
</ul>Security, Governance &amp; Compliance
<ul><li>Ensure compliance with security, privacy, and governance requirements for AI generated marketing workflows.</li>
<li>Define architectural controls for safe execution, auditability, and data protection.</li>
<li>Lead performance optimization, scalability planning, and risk mitigation.</li>
</ul>Cross Functional Collaboration
<ul><li>Work closely with business, product, CRM, and marketing operations teams to translate requirements into scalable technical solutions.</li>
<li>Communicate architectural decisions clearly to both technical and non technical stakeholders.</li>
</ul>Required Skills &amp; Experience
<ul><li>10+ years of software engineering experience with at least 3+ years in a Tech Lead or Architect role.</li>
<li>Strong background in AI/ML systems, including:
<ul><li>LLMs</li>
<li>Agentic architectures</li>
<li>Prompt engineering</li>
<li>RAG pipelines</li>
</ul></li><li>Experience designing complex distributed systems and workflow automation platforms.</li>
<li>Deep understanding of DSL design, interpreters, ASTs, and compiler concepts.</li>
<li>Strong proficiency in Python, TypeScript, or Java.</li>
<li>Experience with cloud native architectures (AWS/Azure/GCP), containers, and microservices.</li>
<li>Proven ability to lead engineering teams, conduct design reviews, and drive technical decisions.</li>
<li>Excellent communication and stakeholder management skills.</li>
</ul>Preferred Qualifications
<ul><li>Experience building AI driven workflow automation or autonomous agent systems.</li>
<li>Familiarity with AMPscript and SSJS.</li>
<li>Background in marketing automation, CRM systems, or customer lifecycle design.</li>
<li>Experience with security, compliance, and governance for AI systems.</li>
<li>Prior experience in fixed bid or outcome based delivery environments.</li>
<li>Experience with event driven architectures and messaging systems.</li></ul></div>

AI ML Tech Lead / AI Architect

The Carlyle Group

<div>
 
 
 Company Profile
 
 
 
 The Carlyle Group (NASDAQ: CG) is a global investment firm with $475 billion of assets under management, across 678 investment vehicles as of March 31, 2026. Founded in 1987 in Washington, DC, Carlyle has grown into one of the world's largest and most successful investment firms, with more than 2,500 professionals operating in 28 offices in North America, Europe, the Middle East, Asia and Australia.
 
 
 
 
 
 Carlyle's purpose is to connect people, ideas, and capital to fuel growth for companies and performance for investors, which range from public and private pension funds to wealthy individuals and families to sovereign wealth funds, unions and corporations. Carlyle invests across three segments - Global Private Equity, Global Credit and Carlyle AlpInvest - and has deep expertise across industries, markets, and geographies.
 
 
 
 
 
 At Carlyle, we believe that a wide spectrum of experiences and viewpoints drives performance and success. Our CEO, Harvey Schwartz, has stated that, "To build better businesses and create value for all of our stakeholders, we are focused on assembling leadership teams with the strongest insights from a range of perspectives." Reflecting this view, emphasis is placed on development, retention and inclusion through our internal processes and seven Employee Resource Groups (ERGs). We cultivate a culture where ideas are openly shared and challenged, connecting diverse expertise and perspectives to drive enduring value.
 
 
 
 
 
 
 Fund or Department Description
 
 
 
 The Data &amp; AI Engineer sits within Carlyle's Enterprise Technology &amp; Data organization and supports firm-wide data and AI initiatives spanning investment platforms, portfolio operations, investor relations, and corporate functions. The role operates within a federated data operating model, partnering with domain engineering teams to implement shared platforms and reusable patterns for data and AI under the technical direction of the Senior AI &amp; Data Architect.
 
 
 
 
 
 
 Position Summary
 
 
 
 The Data &amp; AI Engineer is an experienced, hands-on engineer who turns Carlyle's data and AI architecture into working production systems. Reporting to the Senior AI &amp; Data Architect, this role is responsible for building and operating the pipelines, semantic layers, retrieval systems, and AI-ready data products that power analytics, automation, LLMs, agents, and generative AI applications across the firm.
 
 
 
 
 
 The role requires deep, hands-on expertise across modern data engineering and applied AI engineering. The Data &amp; AI Engineer will implement retrieval-augmented generation (RAG) patterns, embedding and indexing pipelines, vector stores, and semantic models alongside core ELT, streaming, and analytical pipelines - treating LLMs, agents, and copilots as first-class consumers of the data platform.
 
 
 
 
 
 This is a senior individual-contributor engineering role that executes against architectural standards, contributes to their evolution through hands-on learning, and partners closely with data science, AI engineering, governance, and domain teams to deliver trusted, AI-consumable data at enterprise scale.
 
 
 
 
 
 
 What Success Looks Like:
 
 In the first 12 months, this role will deliver foundational AI-ready data pipelines and retrieval components defined in the target-state architecture, productionize one or more priority RAG or agent-grounding use cases, and establish reusable engineering patterns that other domain teams can adopt across the federated data platform.
 
 
 
 
 
 
 In-office requirement:
 
 4 days per week
 
 
 
 
 
 
 Primary Responsibilities:
 
 
 
 
 AI Data Pipelines &amp; Retrieval Systems (&asymp;35%)
 
 
 <ul>
 <li>
 Build and operate AI-ready data pipelines - embedding generation, chunking, indexing, and refresh workflows - that make Carlyle's enterprise data reliably retrievable by LLMs, agents, and generative AI applications.
 </li>
 <li>
 Implement retrieval-augmented generation (RAG) components, including vector store integrations, hybrid search, re-ranking, and grounding logic, against architectural patterns defined by the Senior AI &amp; Data Architect.
 </li>
 <li>
 Develop and maintain tool and function interfaces that allow agents and copilots to query and act on enterprise data safely, with appropriate guardrails, logging, and evaluation hooks.
 </li>
 <li>
 Partner with Data Science and AI Engineering teams to operationalize feature stores, evaluation datasets, and reusable AI data products.
 </li>
 <li>
 Contribute to semantic and context engineering work that powers natural-language analytics, conversational reporting, and AI-driven insights for business users.
 </li>
 </ul>
 
 
 
 
 
 Modern Data Pipeline Engineering (&asymp;30%)
 
 
 <ul>
 <li>
 Design, build, and maintain production-grade ELT, streaming, and transformation pipelines using tools such as dbt, Fivetran and Snowflake.
 </li>
 <li>
 Implement ingestion, modeling, and consumption patterns that meet enterprise standards for scalability, performance, security, resiliency, and cost efficiency.
 </li>
 <li>
 Write clean, well-tested Python and SQL; apply software engineering best practices including version control, code review, CI/CD, modular design, and automated testing.
 </li>
 <li>
 Productionize new sources and domains under the federated operating model, partnering with domain data engineers to apply shared platform capabilities consistently.
 </li>
 </ul>
 
 
 
 
 
 Semantic Layer &amp; Data Product Development (&asymp;20%)
 
 
 <ul>
 <li>
 Implement semantic models, data contracts, and analytical/dimensional models that enable trusted self-service analytics and reliable AI grounding.
 </li>
 <li>
 Build and maintain reusable data products with clear ownership, documented contracts, and contextual metadata suitable for both human and AI consumers.
 </li>
 <li>
 Collaborate with the Senior AI &amp; Data Architect to refine and extend enterprise semantic standards based on what works in production.
 </li>
 <li>
 Support discovery and consumption tooling so that analysts, applications, and agents can find and use data products with minimal friction.
 </li>
 </ul>
 
 
 
 
 
 Data Quality, Observability &amp; AI Trust (&asymp;10%)
 
 
 <ul>
 <li>
 Implement data quality checks, lineage capture, and pipeline observability across both data and AI workloads.
 </li>
 <li>
 Build logging, evaluation, and monitoring components for AI systems - including prompt and response capture, retrieval metrics, and model performance signals - in line with governance standards.
 </li>
 <li>
 Partner with Data Governance to operationalize metadata, stewardship, and access controls, ensuring AI systems consume enterprise data with the same rigor as human users.
 </li>
 <li>
 Surface issues early, propose remediations, and feed lessons learned back into architectural patterns.
 </li>
 </ul>
 
 
 
 
 
 Collaboration &amp; Engineering Craft (&asymp;5%)
 
 
 <ul>
 <li>
 Participate in architectural design reviews and contribute hands-on engineering perspective to evolving patterns and standards.
 </li>
 <li>
 Mentor junior data engineers and analysts on modern data and AI engineering practices.
 </li>
 <li>
 Document patterns, write runbooks, and share knowledge across the federated organization to accelerate adoption of reusable platform capabilities.
 </li>
 </ul>
 
 
 
 
 
 Requirements:
 
 
 
 
 Education &amp; Certifications
 
 
 <ul>
 <li>
 Bachelor's degree, required
 </li>
 <li>
 Concentration in computer science, data engineering, information systems, or a related field, preferred
 </li>
 <li>
 Masters degree, preferred
 </li>
 <li>
 Relevant certifications in cloud, data engineering, analytics, or AI/ML are preferred
 </li>
 </ul>
 
 
 
 
 
 Professional Experience
 
 
 <ul>
 <li>
 6+ years of overall relevant technical experience, required
 </li>
 <li>
 Experience in data engineering, analytics engineering, or platform engineering, with at least 1-2 years of direct, hands-on experience building generative AI or AI/ML systems in production.
 </li>
 <li>
 Proven experience implementing retrieval, grounding, and semantic components for LLM- or agent-based applications, including RAG pipelines, vector stores, embedding workflows, and structured tool use.
 </li>
 <li>
 Hands-on experience with one or more modern AI platforms and tooling categories (e.g., AWS Bedrock, Databricks ML, Snowflake Cortex, OpenAI/Anthropic APIs, LangChain/LlamaIndex or equivalents, MLflow, and vector databases such as Databricks Vector Search, pgvector, or Pinecone).
 </li>
 <li>
 Strong, demonstrable expertise in Python and SQL, with working knowledge of distributed processing frameworks (e.g., Spark).
 </li>
 <li>
 Deep, hands-on experience with modern data stacks - dbt, Fivetran, Snowflake - in AWS-based environments.
 </li>
 <li>
 Track record of building data pipelines and products whose consumers include AI systems, not only BI tools and human analysts.
 </li>
 <li>
 Palantir experience a plus.
 </li>
 <li>
 Experience operating within federated data operating models and complex, regulated enterprise environments; financial services experience preferred.
 </li>
 </ul>
 
 
 
 
 
 Competencies &amp; Attributes
 
 
 <ul>
 <li>
 Demonstrated AI-forward instinct: defaults to asking how AI changes what gets built, rather than whether AI can be added later.
 </li>
 <li>
 Fluency in current AI engineering patterns (RAG, agents, tool use, evaluations, guardrails, observability) and the practical trade-offs involved in shipping them.
 </li>
 <li>
 Strong engineering craft: clean code, automated testing, thoughtful design, and a bias toward production-quality systems over prototypes.
 </li>
 <li>
 Pragmatic, delivery-oriented mindset with strong attention to data quality, AI trust, and long-term maintainability; able to distinguish durable engineering decisions from AI hype.
 </li>
 <li>
 Collaborative partner to architects, data scientists, AI engineers, and domain teams; comfortable operating in a matrixed, federated organization and in high-visibility transformational initiatives.
 </li>
 </ul>
 
 
 
 
 
 Benefits/Compensation
 
 
 
 The compensation range for this role is specific to the applicable office location and takes into account a wide range of factors, including required and preferred skill sets; prior experience and training; and licenses and/or certifications.
 
 
 
 
 
 The anticipated base salary range for this role is $160,000 to $180,000.
 
 
 
 
 
 In addition to base salary, the hired professional will receive a comprehensive benefits package including retirement benefits, health insurance, life and disability insurance, paid time off, paid holidays, family planning benefits, and wellness programs. The hired professional may also be eligible for an annual discretionary incentive program based on individual and organizational performance.
 
</div>

Data & AI Engineer

LN Technologies

Key Responsibilities1. Architecture &amp; Technical Leadership<ul><li>Design end-to-end architectures for AI solutions, IoT platforms, mobile apps, data platforms, and cloud-native systems</li><li>Create architecture diagrams, data models, API specifications, and cloud infrastructure plans</li><li>Define security and compliance frameworks for scalable systems</li><li>Evaluate and recommend emerging technologies aligned with R&amp;D objectives</li><li>Establish architecture standards and best practices across projects</li></ul>2. Project Oversight &amp; Delivery Management<ul><li>Serve as technical lead and solution owner for software development projects</li><li>Collaborate with product managers and engineering teams to break down requirements</li><li>Define technical scope and architecture approach</li><li>Estimate effort and guide sprint planning</li><li>Ensure adherence to code quality, security, and DevOps best practices</li><li>Oversee AI model governance and system integration strategies</li></ul>3. R&amp;D &amp; Innovation Enablement<ul><li>Partner with research teams to translate concepts into technical architectures</li><li>Support technical sections of grant proposals and innovation initiatives</li><li>Prototype new ideas using rapid experimentation and POCs</li><li>Evaluate new frameworks, tools, and platforms for innovation programs</li><li>Drive adoption of emerging technologies in product development</li></ul>4. System Design &amp; Integration<ul><li>Architect scalable cloud-native and microservices-based systems</li><li>Design API-first and event-driven architectures</li><li>Define data platform architectures and integration strategies</li><li>Ensure system scalability, reliability, and performance optimization</li><li>Implement secure system integration across platforms</li></ul>5. Security &amp; Compliance Governance<ul><li>Ensure architecture compliance with security standards (HIPAA, NIST, etc.)</li><li>Define secure authentication, authorization, and data protection models</li><li>Implement risk mitigation strategies in system design</li><li>Enforce cloud security and DevSecOps practices</li></ul>6. Team Management &amp; Leadership<ul><li>Manage and oversee the performance of the engineering and architecture team</li><li>Provide technical mentorship and guidance to team members</li><li>Allocate resources and balance workload across projects</li><li>Conduct performance reviews and support team development plans</li><li>Ensure team adherence to architecture standards and delivery timelines</li><li>Foster collaboration across cross-functional teams</li></ul>7. Stakeholder Collaboration<ul><li>Collaborate with clients, government partners, universities, and internal teams</li><li>Present technical architectures to non-technical stakeholders</li><li>Validate solution feasibility and scalability with cross-functional teams</li><li>Provide technical guidance to engineering and product teams</li></ul>8. Technical Documentation &amp; Knowledge Sharing<ul><li>Document architecture designs and technical decisions</li><li>Prepare architecture guidelines and reusable frameworks</li><li>Conduct knowledge-sharing sessions with engineering teams</li><li>Maintain architecture standards and governance documentation</li></ul>Required Qualifications<ul><li>Bachelor&rsquo;s or Master&rsquo;s degree in Computer Science, Engineering, or related field</li><li>7+ years of experience in software architecture or senior engineering roles</li><li>Hands-on experience with cloud platforms (Azure, AWS, or GCP)</li><li>Experience with AI/ML frameworks (TensorFlow, PyTorch, Azure ML, SageMaker)</li><li>Experience with IoT ecosystems and edge computing</li><li>Strong knowledge of API and microservices architecture</li><li>Experience with mobile applications and data platforms</li><li>Experience leading software development teams</li><li>Strong understanding of scalability, security, and system integration patterns</li></ul>Preferred Qualifications<ul><li>Experience with healthcare systems and HIPAA/FDA compliance</li><li>Experience architecting AI systems for real-time data or predictive analytics</li><li>Experience working in R&amp;D or innovation environments</li><li>Relevant certifications (AWS Solutions Architect, Azure Solutions Architect, TOGAF, PMP, etc.)</li></ul>Job Type: Full-timePay: Rs300,000.00 - Rs450,000.00 per monthApplication Question(s):<ul><li>How many years of experience do you have in software architecture or senior development roles?</li><li>Have you led or managed software developers or Project Managers? If yes, please mention the team size.</li><li>What is your current notice period or earliest joining availability?</li><li>Please share your reason for leaving your current/last employer.</li><li>Please share your current salary.</li><li>Please share your expected salary.</li></ul>Work Location: In person

Solution Architect – Emerging Technologies

Search 8047+ Ai systems architect jobs near you this June. New roles added regularly across leading industries. Apply today.