lead data architect

Hiscox

<div>
 
 Job Type
 
 
 
 Permanent
 
 
 
 Build a brilliant future with Hiscox
 
 
 
 
 Reporting to: Re &amp; ILS Head of Data
 
 
 
 
 Location:
 
 London
 
 
 Type: Permanent
 
 
 
 Role Overview
 
 
 
 The Lead Data Scientist is a crucial member of the
 
 extended
 
 RE&amp;ILS Tech Leadership Team, responsible for building, leading, and nurturing a highly capable (New)
 
 Data Science and Machine Learning
 
 chapter.
 
 
 This role facilitates the successful delivery of new AI Cloud-based solutions against our growing list of business use cases (involving complex Data Analytics, Machine Learning, and AI Agent based solutions).
 
 
 You will co-lead the new Data Science chapter with the RE&amp;ILS Head of Data and help ensure its members provide Data Science capabilities and insights to support decision-making across RE&amp;ILS strategic Value Streams. Whilst the chapter grows, you are expected to be
 
 hands-on
 
 actively helping to architect, design, and build AI or Data Science-based solutions, using internally trained models, LLMs and/or a combination of both.
 
 
 Whilst undertaking this pioneering work it is essential that you and your entire chapter operates within the boundaries of the
 
 Group AI Governance Framework
 
 and maintains rigorous data governance standards. You will be accountable of this adherence.
 
 
 This is likely to be your first managerial role and the first time you have had one or more direct reports &ndash; having previously been a Senior Data Scientist or Machine Learning Engineer yourself. In this role you will be responsible for the management of a 1 to 4 junior and mid-level Hiscox FTEs and Partner resources too. You will be expected to support the Head of Data with attracting and retaining the best talent; helping to manage their career progression and ensure they are highly engaged, find the work fulfilling, and have the ability to rapidly deliver value to the business. As this is likely to be your first managerial role you will be supported and guided by other Data Leaders &ndash; Head of Data and the Hiscox Group Head of Data Science as well.
 
 
 
 About Hiscox Re &amp; ILS
 
 
 
 Hiscox Re &amp; ILS is located in
 
 Bermuda and London
 
 and underwrites reinsurance risks from around the world on behalf of our own and other people&rsquo;s capital. The team of over 100 smart, enthusiastic individuals are committed to providing top quality service to brokers and clients across a broad range of reinsurance products. Our approach is simple; match the best underwriting risk against globally diversified capital. Our proactive, product-led underwriting approach is complemented by a commitment to world class service and purpose-built analytics and systems. We know our people are our biggest asset so finding and retaining the best talent is critical to achieving our vision.
 
 
 
 Key Responsibilities
 
 
 
 <ul>
 <li>
 Chapter Leadership &amp; Talent Management
 </li>
 <li>
 People Management: Lead, recruit, mentor, coach, train, and retain a high-performing chapter of Data Scientists and Machine Learning Engineers (expected to grow to over the next few years), including both Hiscox FTE and Partner colleagues. Inspire chapter members to be curious, embrace new ideas, and nurture psychological safety to help build a high-performance culture.
 </li>
 <li>
 In this role you will manage at least one to two Hiscox data scientists and some partner resources too. You will be expected to manage their performance with relevant PDRs and help to calibrate their overall performance ratings with other people managers in the team. You will work with the Head of Data and Group Head of Data Science to help tailor the training needs to suit each individual and ensure they have the opportunity to grow and progress in their chosen career paths.
 </li>
 <li>
 Culture &amp; Development: Cultivate a collaborative, engaged, and fulfilled team culture. Provide technical leadership, career guidance, and direction to the team, ensuring successful delegation to all chapter members.
 </li>
 <li>
 Best Practice: Ensure the chapter adheres to best practices in data science, AI, and complex analytics, sharing knowledge across the wider Hiscox Analytics and Data community through active participation in Communities of Practice.
 </li>
 <li>
 Mentoring: Mentor and educate others in the various data science techniques, encouraging the team to continually learn &ndash; especially supporting the adoption of LLMs and proprietary models that could significantly accelerate our ability to deploy value.
 </li>
 <li>
 Solution Delivery &amp; ML Ops Excellence
 </li>
 <li>
 Hands-On Architecture: Get hands-on to help architect, design, and build complex models that integrate into existing IT solutions to improve data-driven decision-making throughout the Reinsurance Value Chain. Ensure you and your chapter members work closely with Platform Engineers and Architects to create the most optimal and safe solutions.
 </li>
 <li>
 ML Ops Platform: Actively work with the Head of Platform Engineering to establish and maintain a professional ML Ops platform to facilitate the effective support of developed models in production. This includes embedding a rigorous model evaluation process, ensuring consistent model refresh/rebuild capabilities due to model or data drift, and implementing effective monitoring and alerts against rogue behaviour.
 </li>
 <li>
 Speed of Execution: Empower the chapter to work effectively within autonomous squads, delivering desired and valuable outcomes in an incremental manner while correctly balancing accuracy, fast return on investment, and reliability. Understand when to move on and fail fast &ndash; as a lot of AI use cases will be highly experimental in nature with no guarantees of success.
 </li>
 <li>
 Pipeline Collaboration: Work with Data Engineering and other teams to properly understand and contribute to the requirements and building of necessary data pipelines/products consumed by our models.
 </li>
 <li>
 Strategy, Governance &amp; Innovation
 </li>
 <li>
 Use Case Identification: Continually work with key business leaders, the Head of Data and key stakeholders to identify new use cases and opportunities where data science/AI can deliver a competitive advantage or reduce waste/costs through efficiency gains.
 </li>
 <li>
 Governance &amp; Compliance: Champion data and model governance, working within the Group AI Governance Framework. Ensure a mature understanding of data governance, lineage, and version control, and enforce data privacy by design in all new work.
 </li>
 <li>
 Innovation: Maintain a strong awareness of new trends and techniques in Data Science, AI, and Machine Learning. Drive the identification of innovative data solutions and technologies, and actively undertake Proof of Concepts and R&amp;D to deliver reliable insights and potential next steps quickly.
 </li>
 <li>
 Communication: Ensure clear and effective communication of data science solutions and outcomes across business layers, technical teams, and senior leadership.
 
 
 
 </li>
 </ul>
 
 Required Experience And Structure
 
 
 
 <ul>
 <li>
 Reporting: Reports into the RE&amp;ILS Head of Data with a dotted line into the new Group Head of Data Science.
 </li>
 <li>
 Direct Reports: Direct line management for Data Scientists and Machine Learning Engineers within the RE&amp;ILS Data Science Chapter.
 </li>
 <li>
 Key Partners: RE&amp;ILS CTO, Group Head of Data Science, RE&amp;ILS Head of Data, Product Managers, Product Owners, and strategic partners (e.g. Google Cloud, Microsoft).
 
 
 
 </li>
 </ul>
 
 Candidate Profile
 
 
 
 The successful candidate will be an energetic, highly capable, and passionate Senior Data Scientist who wants to move into their first people management role. You will need to balance technical expertise with effective people management and strategic business partnership. You must be comfortable working in an FCA-regulated business, advocating for a data-driven approach, and driving innovation while maintaining rigorous AI governance standards. You will have a strong determination and resilience and not fear failure or the inevitable setbacks often found in highly experimental work.
 
 
 
 Main Experience Required Is
 
 
 
 <ul>
 <li>
 Deep Interest in Leadership: As an experienced data scientist or machine learning engineer, has previously demonstrated excellent traits in mentoring and coaching more junior team members and has been happy to invest in them - clearly demonstrating they care about other peoples&rsquo; career progression. Also you can evidence some successful business outcomes that you personally drove forward using advanced Data Science/AI &ndash; as this most likely is your first leadership role.
 </li>
 <li>
 Technical Depth: Extensive "hands-on" experience in architecting, designing, and building complex ML models, with a strong understanding of cloud services and the AI/ML/LLM ecosystem.
 </li>
 <li>
 ML Ops Maturity: Proven ability to build, maintain, and support production-deployed models, including establishing effective monitoring, evaluation, and refresh processes.
 </li>
 <li>
 AI and Data Governance: Mature understanding of AI and Data governance - data obfuscation &amp; data privacy laws, data version control, data lineage, and the application of AI governance frameworks in a regulated environment.
 </li>
 <li>
 Stakeholder Management: Experience in managing and driving collaboration with business unit leaders (Underwriters, pricing teams, MI team) and external technology partners like Google Cloud, Snowflake, DataIku and Microsoft.
 </li>
 <li>
 Education: Bachelor's/Master's degree in a quantitative field (e.g., Computer Science, Statistics, Mathematics, Physics, Engineering) or equivalent.
 </li>
 <li>
 Capacity for independent, high-quality research and exploratory data analysis, with the ability to develop and apply novel methodologies to solve ill-defined and challenging business problems.
 </li>
 <li>
 Exceptional proficiency in Python (and R) for data science, coupled with SQL capabilities for data manipulation and extraction. Demonstrable mastery in a wide range of machine learning and statistical modelling techniques, from classical linear models and tree-based methods to advanced deep learning architectures.
 </li>
 <li>
 Real world experience with, or demonstrable capability for, applications of Generative AI, Large Language Models (LLMs), AI Agents and related areas such as Natural Language Processing (NLP) or Computer Vision, relevant to business solutions.
 </li>
 <li>
 Demonstrate a Solid understanding of foundational statistics and experimental design. Pioneer the development and robust deployment of highly impactful machine learning and AI-driven solutions, directly contributing to significant commercial value, quantifiable business growth, or efficiency gains across the organization.
 </li>
 <li>
 Ability to translate complex data science insights into tangible, measurable improvements across critical business processes and key performance indicators, ensuring the real-world adoption and beneficial outcomes of our analytical work.
 </li>
 <li>
 Ability to elevate the technical maturity and capabilities of the data science chapter by shaping and implementing industry-leading AI, data, and analytics techniques, methodologies, and engineering best practices.
 </li>
 <li>
 Educator: shown the ability to train and coach more junior and mid-level team members - ensuring they have excellent career progression and become high performers.
 
 
 
 </li>
 </ul>
 If you are a visionary expert looking for your first leadership role and ready to accelerate our value delivery and champion all things Data Science and AI, we invite you to apply for the position of Lead Data Scientist at Hiscox Re &amp; ILS. Join our team and be a catalyst for AI adoption in a dynamic, forward-thinking organization.
 
 
 
 Work with amazing people and be part of a unique culture
 
</div>

Lead Data Scientist

Accenture

<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 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 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

<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>
Bachelor's Degree or equivalent</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>
Experience in the Agentic, LLM and Generative AI space.</li><li>
Experience architecting and operationalizing LLM driven application architecture patterns.</li><li>
Expertise in coding with Python and engineering, machine learning, deep learning and NLP solutions and applications.</li></ul></div>

AI LLM Technology Architecture Assoc Manager

<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)

Mastercard

<div>Our Purpose
Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we&rsquo;re helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential.
Title and Summary
Senior Principal Agentic Platforms Architect
Overview
 
 
The AI Center of Excellence is seeking a Senior Principal Agentic Platforms Architect to lead the technical vision, architecture, and hands-on development of Mastercard's enterprise agentic AI platforms. This role demands deep, demonstrated expertise in building and shipping production-grade AI and agentic systems at enterprise scale, combined with the architectural rigor required to operate within one of the most highly regulated and security-conscious environments in the world. Reporting to senior leadership, you will own the E2E platform architecture for agentic AI systems, from runtime orchestration and governance infrastructure through to production deployment and operational lifecycle management. This is not a strategy-only role. You will write code, review code, and hold the team to the highest standards of engineering excellence. The right candidate has spent their career building AI systems that run in production, not prototypes, not proofs of concept, but systems that operate at scale, under load, and under scrutiny 
 
Role
 
 
<ul><li>Serve as the principal technical authority for agentic AI platform architecture, owning the design and evolution of runtime environments, governance layers, orchestration engines, and multi-tenancy infrastructure that support enterprise-grade AI agent deployment</li></ul> 
<ul><li>Lead hands-on architecture and development of agentic systems, including agent orchestration frameworks, tool and model integration layers, policy enforcement engines, guardrail systems, behavioral monitoring pipelines, and evaluation infrastructure</li></ul> 
<ul><li>Design and implement scalable, resilient platform architectures that support multi-agent coordination, A2A communication protocols, memory governance, and real-time observability across distributed deployments</li></ul> 
<ul><li>Drive the technical strategy for model integration and abstraction, enabling model-agnostic agent execution across multiple providers while enforcing cost optimization, routing intelligence, and governance controls at the platform level</li></ul> 
<ul><li>Architect and build API-first platform interfaces that enable both internal teams and external consumers to build, deploy, govern, and operate AI agents through programmatic, language-agnostic access patterns</li></ul> 
<ul><li>Define and enforce engineering standards, code quality practices, and architectural governance across the platform engineering team, ensuring every component meets production-grade requirements for security, resilience, performance, and compliance</li></ul> 
<ul><li>Provide deep technical leadership on agentic AI design patterns, including retrieval-augmented generation, tool calling via Model Context Protocol, multi-step orchestration, human-in-loop workflows, and autonomous decision-making architectures</li></ul> 
<ul><li>Act as a trusted technical advisor to senior leadership, translating complex platform architecture decisions into clear, business-oriented language that supports informed decision-making at the executive level</li></ul> 
<ul><li>Mentor and develop a high-performing engineering team, fostering a culture of technical excellence, intellectual honesty, rigorous code review, and continuous improvement</li></ul> 
<ul><li>Produce and maintain authoritative architectural documentation, including system design documents, architecture decision records, and integration specifications that meet Mastercard's engineering principles and security standards</li></ul> 
All About You
 
 
<ul><li>15+ years of hands-on experience in software engineering and AI engineering, with a significant and demonstrable portion spent designing, building, and shipping AI and machine learning systems to production at enterprise scale</li></ul> 
<ul><li>Proven track record of architecting and delivering agentic AI systems, autonomous agents, multi-agent orchestration platforms, or AI-powered automation systems that operate in production with real users, real data, and real consequences</li></ul> 
<ul><li>Deep hands-on coding proficiency. You write production code, you review production code, and you hold others to the same standard. This is a building role, not a diagram-drawing role</li></ul> 
<ul><li>Advanced expertise in Python and modern AI/ML frameworks, with hands-on experience across agentic toolkits such as LangChain, LangGraph, CrewAI, or equivalent orchestration frameworks</li></ul> 
<ul><li>Strong architectural experience with cloud-native platforms including AWS, Databricks, and Kubernetes, with demonstrated ability to design for high availability, fault tolerance, and horizontal scalability</li></ul> 
<ul><li>Deep understanding of AI governance, trust, and safety architectures, including guardrail systems, policy engines, behavioral analytics, evaluation frameworks, red teaming methodologies, and compliance mapping</li></ul> 
<ul><li>Experience designing and building multi-tenant platforms with fine-grained access control, workspace isolation, and enterprise-grade identity and authorization systems</li></ul> 
<ul><li>Proven ability to design API-first architectures, RESTful interfaces, and SDK abstractions that serve both internal platform teams and external consumers</li></ul> 
<ul><li>Strong understanding of data architectures including Delta Lake, data lakehouse patterns, vector databases and retrieval-augmented generation pipelines</li></ul> 
<ul><li>Demonstrated experience with tool calling architectures and model-agnostic integration patterns across multiple LLM providers</li></ul> 
<ul><li>Exceptional communicator who can articulate deeply technical architecture decisions to engineering teams and translate complex platform concepts into business language for non-technical stakeholders</li></ul> 
<ul><li>Known for building trust, holding high standards, and mentoring engineers. You make the people around you better. Motivated by hard problems, not titles. You bring intellectual curiosity, resilience, and relentless energy to complex technical challenges</li></ul> 
<ul><li>Bachelor's degree in Computer Science, Engineering, or a related field. Advanced degree preferred but not required if the experience speaks for itself</li></ul>
Corporate Security Responsibility 
All activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and, therefore, it is expected that every person working for, or on behalf of, Mastercard is responsible for information security and must:<ul><li>
Abide by Mastercard&rsquo;s security policies and practices;</li><li>
Ensure the confidentiality and integrity of the information being accessed;</li><li>
Report any suspected information security violation or breach, and</li><li>
Complete all periodic mandatory security trainings in accordance with Mastercard&rsquo;s guidelines.</li></ul></div>

Senior Principal Agentic Platforms Architect

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