enterprise cloud architect

Capgemini

<div><div><div><div>About the Job you are considering:
We are seeking an experienced Cloud &amp; Data Architecture Consultant to lead enterprise-scale cloud, data, and finance transformation initiatives. The ideal candidate will possess deep expertise in Google Cloud Platform (GCP) architecture, Oracle Integration Cloud (OIC), Enterprise Performance Management (EPM) strategy and tooling, and Databricks-based data platforms. This role will be responsible for defining target-state architectures, assessing and recommending EPM solutions, designing scalable integration frameworks, and enabling cloud-native data modernization programs. The consultant will work closely with business and technology stakeholders to drive architecture governance, solution design, and successful transformation outcomes.
Hybrid working:
The places that you work from day to day will vary according to your role, your needs, and those of the business; it will be a blend of Company offices, client sites, and your home; noting that you will be unable to work at home 100% of the time.
Your Role:
<ul><li>Define and drive enterprise-wide target-state architecture for cloud, data, and finance transformation programs.</li>
<li>Design scalable, secure, and high-performing GCP-based architecture solutions aligned with business and technology objectives.</li>
<li>Assess existing finance and performance management landscapes and provide EPM tooling recommendations based on business requirements, scalability, and future-state strategy.</li>
<li>Architect and govern Oracle Integration Cloud (OIC) solutions for seamless integration across Oracle and third-party applications.</li>
<li>Design and implement Databricks-based modern data platforms supporting analytics, reporting, and AI/ML use cases.</li>
<li>Develop enterprise data architecture standards, data models, and integration patterns to ensure consistency and scalability.</li>
<li>Lead cloud migration and modernization initiatives, including workload assessment, architecture planning, and execution oversight.</li>
<li>Collaborate with finance, business, and technology leadership teams to align architecture decisions with transformation goals.</li>
<li>Establish and enforce architecture governance, best practices, security standards, and design principles.</li>
<li>Conduct architecture reviews, solution assessments, and technical due diligence for new initiatives.</li>
<li>Provide strategic guidance on data management, integration, cloud adoption, and enterprise application modernization.</li>
<li>Facilitate stakeholder workshops, architecture discussions, and executive-level presentations.</li>
<li>Mentor project teams and support delivery organizations in implementing approved architecture solutions</li>
</ul>Your Skills:
<ul><li>Strong expertise in Google Cloud Platform (GCP) Architecture, including cloud-native design patterns and modernization strategies.</li>
<li>Hands-on experience with Oracle Integration Cloud (OIC) and enterprise integration architecture.</li>
<li>Experience evaluating, selecting, and implementing Enterprise Performance Management (EPM) solutions.</li>
<li>Strong knowledge of Databricks, data lakehouse architecture, and modern data engineering principles.</li>
<li>Expertise in Enterprise Data Architecture, data modeling, data governance, and data integration frameworks.</li>
<li>Experience in Cloud Migration, application modernization, and hybrid cloud architectures.</li>
<li>Strong understanding of solution architecture methodologies, security, scalability, and performance optimization.</li>
<li>Familiarity with data warehousing, analytics, reporting, and business intelligence ecosystems</li>
<li>Experience delivering Finance Transformation and enterprise data transformation programs.</li>
<li>Strong understanding of financial planning, forecasting, budgeting, and performance management processes.</li>
<li>Ability to translate business requirements into scalable technology solutions.</li>
<li>Excellent stakeholder management and executive communication skills.</li>
<li>Strong consulting, problem-solving, and analytical capabilities.</li>
<li>Proven experience leading architecture workshops and influencing senior business and technology stakeholders.</li>
<li>Ability to work across cross-functional and distributed teams in a fast-paced environment.</li>
<li>Strong governance, decision-making, and strategic planning skills.</li>
<li>Experience in large-scale enterprise transformation programs.</li>
<li>GCP, Oracle, Databricks, or relevant cloud certifications.</li>
<li>Experience working in consulting environments or leading enterprise architecture engagements.</li>
<li>Familiarity with finance systems, ERP platforms, and performance management ecosystems.</li>
</ul>'We are a Disability Confident Employer:
Capgemini is proud to be a Disability Confident Employer (Level 2) under the UK Government&rsquo;s Disability Confident scheme. As part of our commitment to inclusive recruitment, we will offer an interview to all candidates who:
<ul><li>Declare they have a disability, and</li>
<li>Meet the minimum essential criteria for the role.</li>
</ul>Please opt in during the application process.
Make It Real (what does it mean for you):
<ul><li>You&rsquo;d be joining an accredited Great Place to work for Wellbeing in 2024. Employee wellbeing is vitally important to us as an organisation. We see a healthy and happy workforce a critical component for us to achieve our organisational ambitions.
 
To help support wellbeing we have trained &lsquo;Mental Health Champions&rsquo; across each of our business areas, and we have invested in wellbeing apps such as Thrive and Peppy.</li>
<li>You will be empowered to explore, innovate, and progress. You will benefit from Capgemini&rsquo;s &lsquo;learning for life&rsquo; mindset, meaning you will have countless training and development opportunities from thinktanks to hackathons, and access to 250,000 courses with numerous external certifications from AWS, Microsoft, Harvard ManageMentor, Cybersecurity qualifications and much more.</li>
<li>You will be joining one of the World&rsquo;s Most Ethical Companies&reg;, as recognised by Ethisphere&reg; for 13 consecutive years. We live our values by making ethical business choices every day. Working ethically is at the centre of our culture at Capgemini, meaning you will be helping to create a future we can all be proud of.</li>
</ul>Why you should consider Capgemini:
Growing clients&rsquo; businesses while building a more sustainable, more inclusive future is a tough ask. When you join Capgemini, you&rsquo;ll join a thriving company and become part of a collective of free-thinkers, entrepreneurs and industry experts. We find new ways technology can help us reimagine what&rsquo;s possible. It&rsquo;s why, together, we seek out opportunities that will transform the world&rsquo;s leading businesses, and it&rsquo;s how you&rsquo;ll gain the experiences and connections you need to shape your future. By learning from each other every day, sharing knowledge, and always pushing yourself to do better, you&rsquo;ll build the skills you want. You&rsquo;ll use your skills to help our clients leverage technology to innovate and grow their business. So, it might not always be easy, but making the world a better place rarely is.
About Capgemini:
Capgemini is an AI-powered global business and technology transformation partner, delivering tangible business value. We imagine the future of organisations and make it real with AI, technology and people. With our strong heritage of nearly 60 years, we are a responsible and diverse group of 420,000 team members in more than 50 countries. We deliver end-to-end services and solutions with our deep industry expertise and strong partner ecosystem, leveraging our capabilities across strategy, technology, design, engineering and business operations. The Group reported 2024 global revenues of &euro;22.1 billion.
 
Make it real | www.capgemini.com</div></div></div></div><div></div>

Cloud & Data Architect

Databricks

<div>As the AWS Cloud Partner Solutions Architect for EMEA, you are the single-threaded technical owner of the AWS Databricks alliance across the region &mdash; today a $220M trailing-year consumption book growing at +69.7% YoY, the largest hyperscaler-driven consumption book in EMEA after Microsoft. You are the named Databricks counterpart that the AWS field (PSAs, PDMs, SAs) routes opportunities to, the convener of the EMEA AWS SME community, and the author of the &quot;better-together&quot; technical narrative for Databricks on AWS.
You will engage AWS field and partner leadership, Databricks Field Engineering, and joint customer account teams to drive adoption and consumption of the Data Intelligence Platform on AWS &mdash; through executive alignment, large-scale partner enablement, joint product contribution, and multicloud architectural arbitration in our most strategic EMEA accounts.
Reporting to: Director, Field Engineering (Partner Solutions Architect).
<h2 class="jobSectionHeader">What the role owns:</h2>
<ul><li>AWS-sourced pipeline &amp; EMEA AWS consumption growth &mdash; act as the single EMEA-wide SPOC for the AWS field. Channel AWS-sourced opportunities into Databricks (FY26: 150+ opportunities, ~$60M ARR pipeline, ~$10M realised, 35 net-new greenfield accounts) and carry the FY27 plan-of-record of +60% YoY growth / $297.86M target.</li>
<li>The EMEA AWS SME community &mdash; convene and grow the cross-EMEA group of AWS-deep SAs/SSAs/PSAs (~15&ndash;20 members across UKI, France, Spain, Italy, Benelux, Nordics, MEA, Germany). Land the ASQ-scope expansion (SWAT pattern) and run the joint operating cadence with the Databricks TFC at AWS. This is the mechanism that scales the role from single-person execution to field-distributed delivery.</li>
<li>Partner enablement at scale &mdash; sustain the training engine that feeds pipeline 2&ndash;3 quarters out (300+ AWS Solutions Architects trained in EMEA in FY26; 50+ Databricks xSAs to AWS SA-Associate at zero net cost via the AWS Partner Training Databricks L&amp;E channel; 0-to-GenAI workshops, Lakehouse Days, regional meetups). Maintain the AWS Field Engineering Playbook.</li>
<li>Joint product contribution &amp; reference architectures &mdash; own the assets that lower time-to-first-DBU and unblock deployments: AWS Marketplace QuickLaunch (&quot;Moonwalk+&quot;), Agent Bricks Amazon Bedrock automation, the CloudFormation networking package, the AWS data-exfiltration-protection (DEP) architecture, the remote-S3 strict-residency pattern, and the EMEA cloud-consumption dashboard.</li>
<li>Strategic-account unblock &amp; multicloud arbitration &mdash; lead technical arbitration in EMEA's dense multicloud accounts (BP, EasyJet, Klarna, ABSA, ADIA, Mercedes-Benz, BNP Paribas and others), where a single architectural decision can swing $1M+ ARR.</li>
<li>Executive alignment &amp; external brand &mdash; run QBRs with named AWS regional leaders at BU+1, and be the EMEA technical voice at AWS Summits, DAIS, and Re:Invent EMEA.</li>
</ul><h2 class="jobSectionHeader">The impact you will have:</h2>
<ul><li>Accelerate AWS-sourced pipeline and its conversion to consumption by aligning AWS and Databricks resources and providing the technical expertise to win and expand multicloud accounts.</li>
<li>Shift cloud balance toward AWS across EMEA Diamond and Pearl accounts, protecting and growing Databricks' hyperscaler revenue mix.</li>
<li>Grow field-wide AWS capability &mdash; through the SME community, certification programmes, and reusable architectures &mdash; so AWS depth is distributed across the EMEA field rather than concentrated in one person.</li>
<li>Develop and sustain senior AWS executive relationships that surface new opportunities, sharpen competitive positioning, and anchor joint go-to-market.</li>
</ul><h2 class="jobSectionHeader">What we look for:</h2>
<ul><li>Extensive hands-on experience as a data professional in a modern, cloud-based data stack.</li>
<li>Hyperscaler-alliance experience (AWS strongly preferred) with credibility to be the EMEA SPOC for the AWS field and to convene a cross-region SME community.</li>
<li>AWS-specific technical depth &mdash; PrivateLink, VPC Lattice, IAM, networking/CloudFormation, AWS Marketplace, Amazon Bedrock, and Glue Unity Catalog federation.</li>
<li>At least 3 years of technical pre-sales / sales-methodology experience within a consumption business model.</li>
<li>Ability to collaborate with partner and customer organisations at senior-executive level, aligning their objectives to Databricks products and services.</li>
<li>Proven ability to run training sessions, workshops, and webinars, and to author technical content (whitepapers, reference architectures, solution guides, playbooks).</li>
<li>Coding in a core programming language (Python, Java, or Scala); comfort generating infrastructure-as-code (e.g. CloudFormation).</li>
<li>Designing, implementing, and maintaining end-to-end data architectures for Big Data, Data Warehousing, and AI on MPP-based platforms.</li>
<li>Managing multiple, frequently changing priorities across multiple teams.</li>
</ul>The role requires up to 40% travel across EMEA &mdash; to customer and AWS partner sites, AWS EMEA offices, and the key partnership moments of the year (AWS Summits across EMEA, DAIS, Re:Invent).
<div>About Databricks
Databricks is the data and AI company. More than 10,000 organizations worldwide &mdash; including Comcast, Cond&eacute; Nast, Grammarly, and over 50% of the Fortune 500 &mdash; rely on the Databricks Data Intelligence Platform to unify and democratize data, analytics and AI. Databricks is headquartered in San Francisco, with offices around the globe and was founded by the original creators of Lakehouse, Apache Spark&trade;, Delta Lake and MLflow. To learn more, follow Databricks on Twitter, LinkedIn and Facebook.
 
 
Benefits
 
 
At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees. 
 
 
Our Commitment to Diversity and Inclusion
At Databricks, we are committed to fostering a diverse and inclusive culture where everyone can excel. We take great care to ensure that our hiring practices are inclusive and meet equal employment opportunity standards. Individuals looking for employment at Databricks are considered without regard to age, color, disability, ethnicity, family or marital status, gender identity or expression, language, national origin, physical and mental ability, political affiliation, race, religion, sexual orientation, socio-economic status, veteran status, and other protected characteristics.
Compliance
If access to export-controlled technology or source code is required for performance of job duties, it is within Employer's discretion whether to apply for a U.S. government license for such positions, and Employer may decline to proceed with an applicant on this basis alone.</div></div>

AWS Cloud Partner Solutions Architect — EMEA

<div>Role description 
Infrastructure Solution Architect (cloud architecture,azure iaas,windows server,euc administration)

Location: London (Hybrid, 3 days onsite) 
Employment Type: Permanent / Fixed-Term 


We are seeking a visionary Infrastructure Solution Architect to lead enterprise-wide technology architecture across a broad spectrum of infrastructure domains. This is a strategic leadership role, acting as a trusted advisor to senior stakeholders and shaping the future of complex infrastructure landscapes.

You will play a pivotal role in defining and delivering scalable, secure, and innovative solutions that enable business transformation, operational resilience, and long-term growth. 


Key Responsibilities
<ul>
<li>Define and lead the end-to-end infrastructure architecture strategy, ensuring alignment with digital transformation objectives</li>
<li>Design and govern scalable, high-performance, and secure solutions across multiple domains, including:
<ul><li>Wintel &amp; Linux platforms</li>
<li>Cloud (Azure, AWS, Hybrid environments)</li>
<li>Network (LAN, WAN, SD-WAN, Wireless, Firewalls)</li>
<li>Unified Communications (Cisco, Microsoft Teams)</li>
<li>Mobility &amp; Endpoint Management (Intune, UEM/MDM)</li>
<li>Observability &amp; Monitoring (e.g., ELK, Prometheus, AppDynamics)</li>
<li>End-User Computing (VDI, Citrix, M365, OS lifecycle)</li>
<li>Service Desk &amp; End-User Services (ITSM, automation, CSAT, FCR)</li>
</ul></li><li>Act as a trusted advisor to business and IT leaders, translating strategic objectives into future-ready technical solutions</li>
<li>Lead architecture governance, ensuring compliance with enterprise standards and regulatory frameworks</li>
<li>Drive infrastructure transformation initiatives, including cloud adoption, automation, Zero Trust, SASE, and containerization</li>
<li>Evaluate emerging technologies and provide strategic recommendations</li>
<li>Collaborate with engineering, security, DevOps, and delivery teams to ensure cohesive solution design</li>
<li>Develop reference architectures, blueprints, and roadmaps</li>
<li>Support RFP/RFI responses and pre-sales engagements for large-scale programs</li>
<li>Mentor and guide architects and senior engineers, fostering a culture of technical excellence and innovation</li></ul> 


What We're Looking For
<ul>
<li>Extensive experience in IT infrastructure and architecture, with broad exposure across multiple enterprise domains</li>
<li>Strong track record designing and delivering enterprise-scale infrastructure solutions</li>
<li>Deep expertise across cloud and on-premise environments (Azure, AWS, Hybrid)</li>
<li>Strong understanding of ITSM, ITIL, and service management frameworks</li>
<li>Knowledge of security frameworks, regulatory standards, and resilience planning (e.g., ISO, NIST, DR/BCP)</li>
<li>Exceptional stakeholder management, communication, and leadership skills</li></ul> 


Preferred Certifications
<ul>
<li>TOGAF or equivalent Enterprise Architecture certification</li>
<li>ITIL (v3 / v4)</li>
<li>Microsoft, Cisco, VMware, or Red Hat certifications</li>
<li>Cloud certifications (e.g., AWS Solutions Architect, Azure Solutions Architect)</li></ul> 


Hurry &amp; apply for a more detailed conversation!

#UST 
Skills 
cloud architecture,azure iaas,windows server,euc administration,azure ad,firewall,cloud security architecure,network design,citrix xenapp,netscaler,cisco aci,network security 
About UST 
UST is a global digital transformation solutions provider. For more than 20 years, UST has worked side by side with the world&rsquo;s best companies to make a real impact through transformation. Powered by technology, inspired by people and led by purpose, UST partners with their clients from design to operation. With deep domain expertise and a future-proof philosophy, UST embeds innovation and agility into their clients&rsquo; organizations. With over 30,000 employees in 30 countries, UST builds for boundless impact&mdash;touching billions of lives in the process.</div>

Infrastructure Solution Architect_London (Solution Architect II - Cloud Infrastructure Services)

Accenture

<div>YOU ARE
As a Lead and Principal Infrastructure Architect, you own end-to-end responsibility for designing optimized compute infrastructure for large-scale AI and machine learning systems, including large-scale distributed training environments. You are the authority who translates business goals, SLAs, and client standards into infrastructure architectures that perform at scale while being deliberately engineered for cost-efficiency.
Drawing on deep experience, you weigh multiple viable solutions for any given problem &mdash; across compute, networking, storage, orchestration, and model serving &mdash; and make rational, well-justified architectural decisions tailored to each client's situation, constraints, and standards. You architect and optimize the full computational stack for performance, power, cost, and scalability; design and tune large-scale GPU clusters and distributed training systems; and ensure infrastructure meets security, compliance, and regulatory requirements. As the recognized AI infrastructure expert in at least one hyperscaler cloud (such as AWS, Azure, or Google Cloud), you bring authoritative knowledge of that platform's AI/ML services, accelerators, networking, and cost levers, and apply it to deliver best-in-class solutions. Beyond design, you set technical direction and standards, lead and mentor engineers and architects, partner with clients and stakeholders to shape the infrastructure roadmap, and are ultimately accountable for delivering AI/ML infrastructure that meets business SLAs, controls cost, and scales to enterprise and frontier workloads.
THE WORK<ul><li>
Own the end-to-end architecture and design of optimized compute infrastructure for large-scale AI/ML systems, including large-scale distributed training environments, from concept through delivery.</li><li>
Develop and evaluate architecture alternatives, weighing trade-offs across compute, networking, storage, orchestration, and model serving to make rational, well-justified decisions tailored to each client's situation and standards.</li><li>
Lead architecture assessments and reviews of existing and proposed environments, identifying gaps, risks, bottlenecks, and optimization opportunities, and recommending remediation.</li><li>
Drive architectural decision-making, documenting rationale, trade-offs, and assumptions so decisions are transparent, defensible, and aligned with business SLAs and standards.</li><li>
Define and maintain the AI infrastructure roadmap, planning capacity, scaling, and technology evolution in step with business and product goals.</li><li>
Architect and optimize the full computational stack for performance, power, cost, and scalability, ensuring infrastructure meets business SLAs while being deliberately engineered for cost-efficiency.</li><li>
Design and tune large-scale GPU clusters and distributed training systems, including accelerator selection, interconnect/networking, and storage for high-throughput training workloads.</li><li>
Serve as the authoritative AI infrastructure expert in at least one hyperscaler cloud (AWS, Azure, or GCP), applying deep knowledge of its AI/ML services, accelerators, networking, and cost levers.</li><li>
Design deployment, automation, and CI/CD strategies for reliable, repeatable, and scalable releases of AI systems, models, and data pipelines into production.</li><li>
Establish AI monitoring and observability strategy across InfraOps and MLOps, defining SLAs, SLOs, alerting, and performance/cost tracking, and driving continuous optimization.</li><li>
Integrate AI/ML systems into enterprise environments, ensuring interoperability, security, compliance, and adherence to regulatory and client standards.</li><li>
Lead capacity planning and cost modeling, forecasting compute needs and engineering cost-efficiency into the architecture without compromising performance.</li><li>
Collaborate with clients, stakeholders, and engineering teams to align infrastructure decisions with business outcomes, translating requirements into actionable architecture and standards.</li><li>
Set technical direction, standards, and best practices, mentoring engineers and architects and leading design and code reviews across the team.</li></ul></div>
 
<div>EDUCATION<ul><li>
Bachelor's Degree in Computer Science, Computer Engineering, related Engineering field</li></ul>
BASIC (REQUIRED) QUALIFICATION<ul><li>
Expertise in coding, building, monitoring, troubleshooting applications of AI/ML models; selecting, designing and infrastructure for deploying and running them on premise or on public cloud.</li><li>
Strong understanding of AI and machine learning as a subject.</li><li>
Strong understanding of computing infrastructure, preferred knowledge of AI infrastructure.</li><li>
Proficiency in programming languages such as Python, Java, or C++.</li><li>
Experience with data pipeline and workflow management tools (e.g., Apache Airflow, Kubeflow).</li><li>
Strong problem-solving skills and ability to work in a fast-paced environment.</li><li>
Excellent communication and collaboration skills.</li><li>
Experience in AI/ML infrastructure engineering or related roles on a hyperscaler platform for deploying large scale solutions.</li><li>
Proven experience in leading and managing AI projects and teams.</li><li>
Strong project management skills, with the ability to manage multiple projects simultaneously.</li><li>
Demonstrated experience in evaluating and selecting AI technologies and frameworks.</li><li>
Ability to work with cross-functional teams and drive project alignment.</li></ul></div>

AI Infrastructure Architecture Manager

<div>YOU ARE
As a hands-on Infrastructure Architect, you are an experienced engineer with several years in infrastructure engineering who now takes on more complex, higher-impact work designing and optimizing the AI and machine learning infrastructure that powers real-world applications. Working alongside senior architects and engineers &mdash; and increasingly leading your own workstreams &mdash; you apply proven skills in coding, testing, configuring, deploying, monitoring, and troubleshooting AI systems and the infrastructure they run on. Day to day, you architect and optimize infrastructure components, write and review code and deployment scripts, design and tune cloud and on-premises compute resources such as GPU clusters and distributed training environments, deploy AI systems and models into production, and build and optimize data pipelines that feed AI and ML workflows. You optimize the computational stack for performance, cost, power, and scalability, monitor AI systems and infrastructure health across both InfraOps and MLOps disciplines, perform AI monitoring to track model and system performance, and independently troubleshoot and resolve complex issues across the stack. You also mentor junior engineers, contribute to architectural decisions, and help establish best practices. This is a hands-on, ownership-driven where you apply and deepen your expertise across modern tools and platforms &mdash; including container orchestration, model serving, CI/CD pipelines, InfraOps, MLOps, and AI monitoring &mdash; while making meaningful contributions to infrastructure that enables AI-driven business outcomes.
THE WORK<ul><li>
Write, review, and debug code, scripts, and infrastructure-as-code for AI infrastructure, automation, and tooling, setting standards for quality across the team.</li><li>
Architect, configure, and provision compute resources across cloud and on-premises environments, including GPU clusters and distributed training setups, optimizing for performance and utilization.</li><li>
Design and maintain deployment automation and CI/CD pipelines to support reliable, repeatable releases of AI systems, models, and applications.</li><li>
Deploy AI systems, models, and data pipelines into production, defining and improving the processes and best practices others follow.</li><li>
Lead container orchestration and model serving using tools such as Docker, Kubernetes, and model deployment frameworks.</li><li>
Architect and optimize the computational stack for performance, power, cost, and scalability, balancing trade-offs against business goals.</li><li>
Evaluate and select tools, frameworks, and platforms, making recommendations that shape the infrastructure roadmap.</li><li>
Integrate AI models and systems into existing enterprise systems, ensuring interoperability, security, and regulatory compliance.</li><li>
Own AI monitoring and infrastructure health across InfraOps and MLOps, tracking performance, reliability, and utilization, and driving remediation.</li><li>
Independently troubleshoot and resolve complex issues across the computational stack &mdash; hardware, networking, software, and models &mdash; and lead root-cause analysis.</li><li>
Mentor junior engineers and lead code reviews, providing technical direction and supporting their growth.</li><li>
Define and document architecture standards, processes, and procedures, and apply security, cost-efficiency, and scalability best practices across the infrastructure.</li></ul></div>
 
<div>EDUCATION<ul><li>
Bachelor's Degree in Computer Science, Computer Engineering, related Engineering field</li></ul>
BASIC (REQUIRED) QUALIFICATION<ul><li>
Experienced with coding, building, monitoring, troubleshooting applications of AI/ML models; selecting, designing and infrastructure for deploying and running them on premise or on public cloud.</li><li>
Strong understanding of AI and machine learning as a subject.</li><li>
Strong understanding of computing infrastructure a subject, preferred knowledge of AI infrastructure.</li><li>
Proficient in programming languages such as Python, Java, or C++.</li><li>
Experience with data pipeline and workflow management tools (e.g., Apache Airflow, Kubeflow).</li><li>
Strong problem-solving skills and ability to work in a fast-paced environment.</li><li>
Excellent communication and collaboration skills.</li><li>
Experience in AI/ML infrastructure engineering or related roles on a hyperscaler platform for deploying large scale solutions.</li></ul></div>

AI Infrastructure Architecture Specialist

<div>YOU ARE
As an experienced and senior AI/LLM Architect, you will play a pivotal in designing and delivering end-to-end AI platform architectures that power the modern, reinvented enterprise. Operating at the intersection of business and engineering, you will own the technical design of advanced AI systems &mdash; spanning classical machine learning, generative AI, and agentic systems &mdash; ensuring they are purposefully architected to meet client business objectives and enterprise-grade standards.
Within this scope, you will take deep ownership of one or more critical architecture domains &mdash; such as agentic application design, AI security and trust, AI operations and observability, data and knowledge engineering, or model platforms and inference &mdash; serving as the lead authority in your domain across client engagements. You will develop and maintain specialized expertise in the technologies, patterns, and emerging practices within your domain, bringing that depth to bear in shaping architecture decisions, accelerating delivery, and building reusable assets that extend across the practice. You will evaluate, select, and apply the right design patterns, technical frameworks, and tools within your domain and across the broader AI architecture &mdash; ensuring cohesion across the full system. This includes architecting AI agents encompassing multi-agent orchestration, tool use, skills use, and memory systems, as well as the integration of fine-tuned foundation models and classical ML models into scalable, production-ready platforms.
A critical dimension of this is owning the design of a comprehensive AI context layer &mdash; drawing on enterprise knowledge bases, structured and unstructured data sources, and domain-specific content &mdash; to ground AI systems in the realities of each client's business and ensure outputs are accurate, trustworthy, and impactful. As the technical authority on your domains, you will lead architecture decisions and be accountable for ensuring systems meet rigorous non-functional requirements across security, observability, governance, performance, and scalability. You will produce and own the architecture artifacts that shape delivery &mdash; including architecture decision records (ADRs), component and data flow diagrams, and integration specifications &mdash; and provide the technical leadership that enables cross-functional teams of data engineers, ML engineers, and application developers to execute with clarity and confidence.
Your contributions will be instrumental in shaping how clients adopt and scale AI, pushing the boundaries of what these systems can achieve and delivering measurable, lasting business value.
THE WORK<ul><li>
Translate business strategy into a technical vision by defining the non-functional requirements (NFRs) necessary to meet operational goals for performance, reliability, and cost.</li><li>
Lead stakeholder workshops to align on technical feasibility, define project scope, and manage expectations with clients and leadership.</li><li>
Drive the technology selection process, evaluating build-vs-buy decisions for AI platforms (e.g., Arize, LangSmith) and foundational models.</li><li>
Architect model- and tool-agnostic multi-agent systems governed by an MCP Control Plane.</li><li>
Design and implement the Agent Registry as the mandatory system of record and the AI Gateway for runtime policy enforcement.</li><li>
Design and implement a certification gate to ensure no uncertified agents enter production, validating identity, policies, and evaluation metrics.</li><li>
Design, implement, and abstract core agent services, including a first-class abstracted memory service with semantic, episodic, and procedural endpoints.</li><li>
Architect the end-to-end data pipeline for AI systems, including data ingestion, preprocessing, and synchronization for fine-tuning and RAG.</li><li>
Design and implement the context layer&mdash;spanning knowledge graphs, vector search, and semantic retrieval&mdash;to create reliable, grounded RAG pipelines.</li><li>
Architect foundation model adaptation strategies, including dynamic, cost-and-performance-aware model routing and selection.</li><li>
Design, implement, and prototype high-throughput, low-latency inferencing solutions using techniques like response caching and request batching.</li><li>
Define security, governance, and observability as centrally-enforced, by-design controls for all AI systems.</li><li>
Architect a robust, defense-in-depth security framework, including per-agent identity with IAM/IAP binding and layered guardrails.</li><li>
Design and implement FinOps controls enforced at the AI Gateway, including token budgets, cost-center labeling, and threshold alerts.</li><li>
Establish the framework for comprehensive system evaluation, adopting productized tools and instrumenting observability with OTel</li><li>
Define and maintain the enterprise-wide AI reference architecture, reusable design patterns, and a library of approved software components.</li><li>
Independently design, implement, build, and deliver proof-of-concept prototypes and foundational software components to validate architectural decisions.</li><li>
Produce and own authoritative architecture artifacts, including blueprints, sequence diagrams, design specifications, and Architectural Decision Records (ADRs).</li><li>
Mentor and guide cross-functional engineering teams (data, ML, application) on architectural best practices and design patterns.</li><li>
Continuously research and integrate emerging AI patterns, frameworks, and technologies to maintain a forward-looking architecture.</li></ul></div>
 
<div>EDUCATION<ul><li>
Bachelor's Degree or equivalent</li></ul>
BASIC (REQUIRED) QUALIFICATION<ul><li>
Experience in designing &amp; deploying enterprise grade advanced AI solutions using agentic, generative and classical AI/ML using at least one cloud vendor.</li><li>
Experience in the Agentic, LLM and Generative AI space.</li><li>
Coding experience using Python</li><li>
Experience architecting and operationalizing LLM driven application architecture patterns.</li><li>
Expertise in coding engineering, machine learning, deep learning and NLP solutions and applications.</li><li>
Experience as a machine learning architect in the industry designing big data, machine learning. large scale analytical engineering solutions.</li></ul></div>

AI LLM Technology Architecture Manager

<div>YOU ARE
As a hands-on AI/LLM Architect, you will be at the heart of designing and building advanced AI systems that power the modern enterprise. This is a deeply technical, hands-on &mdash; you will spend the majority of your time in the architecture and engineering of real-world AI solutions across classical machine learning, generative AI, and agentic systems, delivering these within active client engagements. You will translate requirements into concrete architecture decisions: selecting design patterns, evaluating and benchmarking technical frameworks, assembling reusable components, and making deliberate technology choices that balance innovation with enterprise-grade reliability. You will design and build AI agent architectures &mdash; including multi-agent orchestration, tool use, skills use, and memory systems &mdash; and work hands-on with foundation models through fine-tuning, retrieval-augmented generation (RAG), and custom model integration. A part of your work will also involve engineering the AI context layer that makes these systems intelligent in practice &mdash; connecting enterprise knowledge bases, structured and unstructured data sources, and domain-specific content so that AI outputs are grounded, accurate, and relevant to the client's business. You will design and validate systems against enterprise non-functional requirements across security, observability, governance, performance, and scalability. A core output of this is the production of tangible engineering and architecture deliverables. This means writing and owning software components &mdash; building, integrating, and testing AI system modules as a practitioner &mdash; alongside producing detailed architecture artifacts including architecture decision records (ADRs), component diagrams, data flow diagrams, and integration specifications that guide and enable broader engineering teams. You will work with cross-functional delivery teams alongside data engineers, ML engineers, and application developers, and this is an opportunity to develop deep expertise across the full AI architecture stack, sharpen your engineering instincts on complex, real-world problems, and build a foundation for growing into a lead or principal architect over time.
THE WORK<ul><li>
Independently design, build, and deliver software components across the AI architecture &mdash; owning them end to end from design through implementation, integration, and testing as a hands-on practitioner</li><li>
Design and build AI agent architectures &mdash; including individual agents, their prompts, tools, and skills, multi-agent orchestration, and memory systems &mdash; making deliberate design pattern and technology choices</li><li>
Design and implement agent orchestration patterns that handle task handoffs, communication, state management, and error recovery, validating them through hands-on prototyping</li><li>
Evaluate multiple design options and technical approaches, making deliberate, justified design choices that balance capability, cost efficiency, performance, and enterprise-grade reliability</li><li>
Design, build, and run evaluation strategies and harnesses that measure agent and system quality on metrics such as accuracy, relevance, and faithfulness, translating findings into design improvements</li><li>
Architect and implement foundation model integrations &mdash; selecting the right models, invocation patterns, and customization approaches (fine-tuning, RAG, custom integration) based on capability, cost, and performance trade-offs</li><li>
Design and build model adaptation and fine-tuning pipelines, applying working knowledge of transformer-based architectures to inform model selection and optimization</li><li>
Design and build the AI context layer &mdash; including context graph design and ingestion pipelines that parse, chunk, enrich, and index structured and unstructured enterprise content, and the retrieval components that ground AI outputs in the client's knowledge</li><li>
Build embedding, vector storage, and retrieval (semantic, hybrid, reranking) into end-to-end RAG pipelines, applying integration patterns that connect to enterprise data sources</li><li>
Design and implement context assembly and memory components that manage prompts, context windows, and conversational state for grounded, accurate outputs</li><li>
Identify, design, and build reusable components and solution patterns that accelerate delivery and can be templated across engagements</li><li>
Design for cost efficiency and performance &mdash; optimizing model usage, inference patterns, caching, and resource utilization to meet target latency, throughput, and cost objectives</li><li>
Design, build, and validate systems against enterprise non-functional requirements &mdash; implementing guardrails, prompt-injection defenses, PII handling, and access controls for security and Responsible AI</li><li>
Build governance controls including versioning, audit logging, and lineage tracking, and produce the model documentation that keeps systems auditable</li><li>
Build observability into systems &mdash; logging, tracing, monitoring, alerting, and cost tracking &mdash; to ensure AI solutions remain healthy, performant, and scalable in production</li><li>
Produce detailed architecture artifacts &mdash; including architecture decision records (ADRs), architecture blueprints, design documents, agent orchestration and integration pattern specifications, component and data flow diagrams &mdash; that guide and enable broader engineering teams</li><li>
Continuously learn, evaluate, and apply new design patterns, frameworks, and technologies across the fast-evolving AI landscape, balancing innovation with enterprise-grade reliability</li><li>
Collaborate with cross-functional delivery teams &mdash; data engineers, ML engineers, and application developers &mdash; to translate requirements into concrete architecture decisions that meet stakeholder needs</li></ul></div>
 
<div>EDUCATION<ul><li><ul><li>Bachelor's Degree or equivalent</li></ul></li></ul>
BASIC (REQUIRED) QUALIFICATION<ul><li>
Experience in designing &amp; deploying AI / ML solutions using at least one cloud vendor as an AI/ML architect.</li><li>
Recent experience in the Agentic, LLM and Generative AI space.</li><li>
Experience architecting and operationalizing LLM driven application architecture patterns.</li><li>
Experience coding and engineering, machine learning, deep learning and NLP solutions and applications. Coding experience using python</li></ul></div>

AI LLM Technology Architecture Specialist

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