ai architect

Talan

<div><b>Company Description</b><p><br>
Talan Data x AI is a leading Data Management and Analytics consultancy, working closely with leading software vendors and top industry experts across a range of sectors, unlocking value and insight from their data. At Talan Data x AI, innovation is at the heart of our client offerings, and we help companies to further improve their efficiency with modern processes and technologies, such as Machine Learning (ML) and Artificial Intelligence (AI).</p><b><br>
Job Description</b><p><br>
As our AI practice scales, we are seeking an experienced AI Architect / Engineer to act as the senior technical authority shaping how we design and deliver next-generation AI solutions. This is a role for someone who has moved beyond building individual models to architecting entire systems: agentic platforms, LLM-powered applications, and the production infrastructure that makes them dependable at enterprise scale.</p><p>
You will sit at the intersection of strategy and delivery, operating across multiple client engagements simultaneously. In the early phases you will shape solutions, validate technical approaches, and translate ambiguous business problems into robust, deployable architectures. As engagements mature, you will provide the technical oversight that keeps delivery teams aligned to the original design intent. Your influence will be felt in pre-sales conversations, solution blueprints, and the technical standards that define how our practice builds.</p><p>
This is a hands on architectural role, not a purely advisory one. We expect you to remain close to the code, to prototype where it accelerates a decision, and to set a credible technical example for the engineers and data scientists you guide.</p><p><b>
Required skills &amp; experience:</b></p><ul><li>
Lead end-to-end solution architecture for AI and LLM-based systems, spanning data, integration, infrastructure, and deployment.</li><li>
Design agentic architectures and multi-agent orchestration patterns, defining how LLMs, tools, APIs, and retrieval layers interact reliably.</li><li>
Shape early-stage engagements (scoping, solutioning, estimation, and pre-sales) turning client ambition into deliverable technical plans.</li><li>
Provide technical validation and quality oversight across several concurrent projects, mitigating risk before it reaches delivery.</li><li>
Define LLM integration and prompting strategies, including RAG, fine-tuning, evaluation, and guardrailing approaches.</li><li>
Establish MLOps and LLMOps practices that ensure solutions are observable, testable, and production ready.</li><li>
Set architectural standards, reusable patterns, and reference implementations that raise the quality bar across the practice.</li><li>
Partner with Responsible AI colleagues to embed safety, security, and governance into solution designs from the outset.</li></ul><br>
<b>
Qualifications</b><p><b><br>
To qualify for this role, you must have:
</b></p><ul><li>Minimum 5 years in AI architecture or AI engineering, ideally 10+, with a track record of production deployments.</li><li>
Deep expertise in LLMs, agent frameworks (e.g. LangGraph, AutoGen, Semantic Kernel), API design, and orchestration.</li><li>
Strong cloud engineering experience.</li><li>
Proven MLOps / LLMOps capability &mdash; CI/CD for models, monitoring, evaluation, and cost optimisation.</li><li>
Hands-on software engineering background (Python and modern tooling) underpinning your architectural decisions.</li><li>
Comfort managing multiple engagements at once, with pragmatic judgement on trade-offs and delivery risk.</li></ul><p><b>
Ideally, you&rsquo;ll also have, but not essential:</b></p><ul><li>
Experience in consulting, professional services, or pre-sales environments.</li><li>
Exposure to enterprise-scale, regulated AI deployments (financial services, public sector, healthcare).</li><li>
Familiarity with vector databases, knowledge graphs, and advanced retrieval techniques.</li><li>
Relevant cloud or AI certifications.</li></ul><p><b>
You must be:</b></p><ul><li>
Willing to work on client sites, potentially for extended periods.</li><li>
Willing to travel for work purposes and be happy to stay away from home for extended periods.</li><li>
Eligible to work in the UK without restriction.</li></ul><p></p><p><br>
#TalanUK #LI-HB1</p><b><br>
Additional Information</b><p><b><br>
Compensation &amp; Benefits Package:</b></p><ul><li>
BDP Plus &ndash; A reward programme whereby you accrue points to trade against a 3-month paid sabbatical or cash equivalent.</li><li>
25 days holiday + bank holidays.</li><li>
5 days holiday buy/sell option.</li><li>
Private medical insurance.</li><li>
Life cover.</li><li>
Cycle to work scheme.</li><li>
Eligibility for company pension scheme (5% employer contribution, salary sacrifice option).</li><li>
Employee assistance programme.</li><li>
Bespoke online learning via Udemy for Business.</li></ul><p><b>
Why join:</b></p><p>
You will be a founding technical voice in a practice that is investing decisively in AI. The work is varied, intellectually demanding, and commercially significant, you will shape solutions that reach production, not slideware. You will have the autonomy to define how we build, the support of a collaborative multidisciplinary team, and a clear platform to grow into a practice-leading technical figure.</p></div>

AI Architect / Engineer (Agentic AI & LLM Architecture)

Luxoft

<div><div><h5 class="jobSectionHeader"><b>Project description
</b></h5><p>We are seeking a Senior Semantic Engineer to design and implement semantic data frameworks that provide a shared structure for enterprise data. In this role you will focus on building and maintaining ontologies and knowledge graphs, enforcing semantic validation rules for data quality, and collaborating with AI teams to integrate these semantic structures into intelligent applications. The position is industry-agnostic, emphasizing strong semantic web expertise and the ability to apply it in any enterprise context.
</p></div><div><h5 class="jobSectionHeader"><b>Responsibilities</b></h5>
<div><div><p>Ontology Design &amp; Maintenance: Design, develop, and maintain ontologies (using OWL/RDF or similar) that model key enterprise data domains and relationships, ensuring a consistent and shared data vocabulary across the organization. This includes collaborating with domain experts to capture real-world concepts and validate that the ontology accurately represents business knowledge.
</p></div><div><p>Knowledge Graph Development: Build and manage enterprise knowledge graphs based on the defined ontologies, linking diverse data sources into a unified graph data model. This involves configuring graph databases or triple stores, populating the knowledge graph with data (RDF triples), and optimizing it for query performance and scalability.
</p></div><div><p>Semantic Querying (SPARQL): Create and optimize SPARQL queries to enable efficient retrieval, integration, and analysis of data from the knowledge graph. You will develop semantic queries and endpoints that support advanced search and analytics use cases, making it easier for others to retrieve insights from linked data.
</p></div><div><p>Validation Rules &amp; Data Quality: Implement semantic validation rules and consistency checks (e.g., using SHACL or OWL constraints) to ensure data integrity and quality within the ontology and knowledge graph. You will define and enforce data modelling conventions and business rules so that enterprise data conforms to the ontology's standards and remains interoperable across systems.
</p></div><div><p>Integration with Enterprise Systems: Work closely with software engineers, data architects, and IT teams to integrate the ontology and knowledge graph into the organization's existing data infrastructure and workflows. This includes embedding semantic models in data pipelines, APIs, and databases, so that enterprise applications can produce and consume linked data seamlessly.
</p></div><div><p>Collaboration &amp; Cross-Functional Support: Collaborate with cross-functional teams and stakeholders. For example, partner with AI/ML teams to incorporate the knowledge graph into AI-driven solutions, and team up with business analysts or data stewards to align the semantic models with business needs. You will communicate semantic concepts to non-technical stakeholders, providing training or documentation to ensure adoption of the semantic framework across the organisation.
</p></div><div><p>Integration with AI Agents: Work with AI agents and large language model (LLM) teams to leverage the ontology and knowledge graph for intelligent applications. For instance, you might enable an AI chatbot to use the knowledge graph for more context-aware responses, or develop mechanisms for AI systems to perform reasoning over the ontologies. This responsibility ensures that semantic data structures enhance AI initiatives (e.g. improving context, disambiguation, and knowledge retrieval in AI workflows).
</p></div><div><p>Standards &amp; Best Practices: Stay current with emerging semantic web standards, tools, and best practices. Continuously improve the semantic architecture by adopting relevant metadata standards and ensuring alignment with industry best practices for ontologies and knowledge graphs. You will also contribute to establishing internal guidelines and best practices for semantic data management, promoting a culture of well-structured, semantically-rich data across the enterprise.
</p></div></div></div><div><h5 class="jobSectionHeader"><b>Skills</b></h5>
<p>Must have
</p><div><div><p>Ontology Design &amp; Maintenance: Design, develop, and maintain ontologies (using OWL/RDF or similar).
</p></div><div><p>Semantic Web Proficiency: Strong knowledge of semantic web technologies and standards
</p></div><div><p>specifically, hands-on proficiency with OWL (Web Ontology Language) and RDF (Resource Description Framework) for ontology modelling, as well as SPARQL for querying graph data.
</p></div><div><p>Knowledge Graph Experience: Practical experience building or maintaining knowledge graphs or linked data systems in an enterprise setting.
</p></div><div><p>Data Modelling &amp; Integration Skills: A solid understanding of data modelling principles, data architecture, and integrating heterogeneous data sources. You should be capable of abstracting real-world entities into a semantic schema and mapping relational or NoSQL data to an ontology.
</p></div><div><p>Programming Skills: Proficiency in at least one programming or scripting language (such as Python, Java, or similar)
</p></div></div><p>Nice to have
</p><div><div><p>Metadata Standards: Familiarity with metadata standards and vocabularies such as Dublin Core, schema.org, or other industry-specific ontologies/taxonomies. Experience applying these standards to annotate or integrate data
</p></div><div><p>AI and LLM Integration: Experience working on projects that involve AI agents or large language models, where ontologies or knowledge graphs were used to improve AI performance.
</p></div><div><p>Enterprise System Integration: Proven experience integrating semantic technologies into existing enterprise systems or data platforms.
</p></div><div><p>Tools &amp; Platforms: Hands-on experience with ontology and knowledge graph tools is beneficial.
</p></div></div></div><div><h5 class="jobSectionHeader"><b>Other</b></h5>
<div><div><div><p>Languages</p>
<p>English: C1 Advanced
</p></div></div><div><div><p>Seniority</p>
<p>Senior</p></div></div></div></div></div>
<br><div><div><div><p>London, United Kingdom of Great Britain and Northern Ireland
</p></div><p>Req. VR-119923
</p></div><div></div><div><div><p>AI/ML
</p></div><div><p>BCM Industry</p>
</div><div><p>03/07/2026
</p></div><div><p>Req. VR-119923</p></div></div></div><p></p>

Semantic AI/ML Architect

Accenture

<div><p><b>YOU ARE</b></p><p>
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.
</p><p>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.
</p><p></p><p>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.
</p><p>You will design and validate systems against enterprise non-functional requirements across security, observability, governance, performance, and scalability.
</p><p>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.
</p><p></p><p>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.</p><p><b>
THE WORK</b></p><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>
<br>
<div><p><b>EDUCATION</b></p><ul><li>
Bachelor's Degree or equivalent</li></ul><p><b>
BASIC (REQUIRED) QUALIFICATION</b></p><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><p><b>YOU ARE</b></p><p>
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.</p><p></p><p><b>
THE WORK</b></p><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>
<br>
<div><p><b>EDUCATION</b></p><ul><li><ul><li>Bachelor's Degree or equivalent</li></ul></li></ul><p><b>
BASIC (REQUIRED) QUALIFICATION</b></p><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

Mastercard

<div><p><b>Our Purpose</b></p><p></p><p><i>
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.</i></p><p></p><p><b>
Title and Summary</b></p><p></p>
Senior Principal Agentic Platforms Architect<p></p>
Overview
<br>
<br>
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<br>
<br>
Role
<br>
<br>
<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><br>
<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><br>
<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><br>
<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><br>
<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><br>
<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><br>
<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><br>
<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><br>
<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><br>
<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><br>
All About You
<br>
<br>
<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><br>
<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><br>
<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><br>
<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><br>
<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><br>
<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><br>
<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><br>
<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><br>
<ul><li>Strong understanding of data architectures including Delta Lake, data lakehouse patterns, vector databases and retrieval-augmented generation pipelines</li></ul><br>
<ul><li>Demonstrated experience with tool calling architectures and model-agnostic integration patterns across multiple LLM providers</li></ul><br>
<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><br>
<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><br>
<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><p></p><p><b>
Corporate Security Responsibility</b></p><p><br>
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:</p><ul><li><p>
Abide by Mastercard&rsquo;s security policies and practices;</p></li><li><p>
Ensure the confidentiality and integrity of the information being accessed;</p></li><li><p>
Report any suspected information security violation or breach, and</p></li><li><p>
Complete all periodic mandatory security trainings in accordance with Mastercard&rsquo;s guidelines.</p></li></ul></div><p></p>

Senior Principal Agentic Platforms Architect

Bluefish AI

<div><p><b>About the Position</b></p>
<p>
As a Staff AI Engineer, you&rsquo;ll serve as a technical leader for our LLM-powered products at the forefront of marketing and advertising technologies. You&rsquo;ll own critical architectural decisions, set quality bars, and lead multi‑team initiatives that drive measurable outcomes.</p>
<p>
As our Staff AI Engineer, you will lead the vision and execution of our data platform and LLM-powered products. You&rsquo;ll own critical architectural decisions across data pipelines, model integration, model deployment and evals: turning high-level product and business requirements into robust, scalable data products that drive measurable outcomes for our Fortune 500 customers. This role spans data backend and ML engineering: from the reliability and cost efficiency of our pipelines to the outcome and performance of LLM-enabled features. You&rsquo;ll raise the bar for data literacy across the department, craft and collaboration.</p>
<p>
You&rsquo;ll be accountable for the health, cost, and evolution of key data product and data platforms, partnering closely with full-stack engineers, product, design, and devops to deliver outcomes our customers can trust.</p>
<p>
This role presents an exciting opportunity to shape the future of AI-driven technologies and make meaningful contributions to real-world applications.</p>
<p>
The role is linked with our location in London, but we are flexible about hybrid or remote work in The United Kingdom.</p>
<p><b>
What You&rsquo;ll Be Doing</b></p>
<ul>
<li>Lead end-to-end architecture for data platforms and pipelines: scraping, data extraction, transformation, storage, serving, and ML/LLM integration, balancing performance, reliability, security, and cost.</li>
<li>Incrementally scale pipelines and systems: design safe rollout plans and north star data-quality metrics to handle customer and traffic growth without impacting production.</li>
<li>Translate business goals into actionable data products: assess high-level requirements, carve clear problem spaces, draft crisp RFCs, and sequence work into deliverable projects for the team.</li>
<li>Establish and enforce engineering standards: testing strategy, evals, observability, data contracts, and security practices across services. Think through short-term and long-term goals to come up with fast go-to-market products, while planning ahead for productization.</li>
<li>Up‑level the org: lead architecture reviews, codify patterns, mentor Senior Engineers, and multiply impact through documentation, code reviews, and pairing.</li>
<li>Startup‑ready: flexible, comfortable with ambiguity and constant change; proactive about process, documentation, and reliability without over‑engineering.</li>
<li>Lead the collaboration and define how AI engineers work cross-functionally with software engineers, devops, product managers and designers, to conceptualize and shape innovative and impactful solutions. Provide mentorship to junior team members and cultivate a culture of collaboration and innovation.</li>
<li>Ship meaningful experiments: prototype data/ML capabilities, evaluate feasibility and ROI, and make pragmatic calls on productionalizing with an eye on operating costs and risk.</li>
</ul>
<p><b>Qualifications</b></p>
<ul>
<li>8+ years building and operating production data systems, including leading cross-cutting architectural changes, and deploying LLMs in real‑world scenarios at scale.</li>
<li>Deep experience Python and modern service architectures; strong system design and data modeling fundamentals.</li>
<li>Extensive experience with training and deploying machine learning models, particularly within the NLP/LLM domain. Proficiency in Python. Familiarity with infrastructure as code, CI/CD, and cloud infrastructure.</li>
<li>Fluency in operational maturity: SLOs, on‑call/incident practices, and observability.</li>
<li>Strong analytical and problem-solving abilities, with a bias towards action and outcomes. Experience with data preprocessing, feature engineering, and model evaluation techniques.</li>
<li>Excellent communication skills, with the ability to explain complex technical concepts to non-technical stakeholders. Demonstrated leadership experience, with the ability to guide and inspire a team.</li></ul><br>
<p></p>
<p><b>
About Bluefish:</b></p>
<p>
Bluefish believes that AI represents the next major chapter of the internet &ndash; and that consumers will increasingly use AI to consume information and media online. On this new AI internet, brands will need new tools and technologies to tell their stories to consumers online &ndash; and a new marketing ecosystem will be created around AI. Bluefish is building the platform that helps brands engage consumers on this new AI channel, with powerful enterprise tools to manage AI brand safety and engage consumers with thoughtful and personalized AI marketing experiences.</p>
<p>
The Bluefish team is a tight-knit group of mar-tech industry veterans who previously helped build foundational ad-tech platforms now owned by Meta and Microsoft. Bluefish, the leading AI marketing platform for the Fortune 500, recently announced a $43 million Series B growth financing co-led by Threshold Ventures and NEA with participation from multiple enterprise investors including Amex Ventures, TIAA Ventures, Salesforce Ventures, and continued participation from Bloomberg Beta. We are a globally distributed team, with business operations based in New York City and engineering based in Europe and New York.</p>
<p><b>
Why Bluefish &amp; Our Values:</b></p>
<ul>
<li>Unique opportunity to join on the ground floor of a fast-moving startup building at the center of AI</li>
<li>Tackle challenging and abstract problems while disrupting the $300BN legacy mar-tech industry</li>
<li>Join an experienced high-performing team where you will have immediate ownership and impact</li>
<li>Experience a true meritocracy with significant career growth upside as the business scales</li>
</ul>
<p><b>Our Values:</b></p>
<ul>
<li>Demonstrated grit and resourcefulness - you find creative solutions, adapt quickly, and aren&rsquo;t afraid to roll up your sleeves in a fast-paced, evolving environment.</li>
<li>Thoughtful and strategic thinker with the ability to anticipate needs, ask the right questions, and deliver insights that truly drive value.</li>
<li>Proven ability to collaborate across functions and roles, fostering open communication and shared ownership of outcomes.</li>
<li>Commitment to craftsmanship - you take pride in delivering high-quality work, paying attention to the small details while keeping the big picture in mind.</li>
<li>Self-starter with a proactive mindset and a strong sense of accountability - able to drive projects forward independently while staying aligned with team goals.</li>
<li>Comfortable navigating ambiguity and learning new tools, processes, or technologies to get the job done.</li></ul></div>

Staff AI Engineer

<div><p><b>YOU ARE</b></p><p></p><p>
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.</p><p></p><p><b>
THE WORK</b></p><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>
<br>
<div><p><b>EDUCATION</b></p><ul><li>
Bachelor's Degree in Computer Science, Computer Engineering, related Engineering field</li></ul><p></p><p><b>
BASIC (REQUIRED) QUALIFICATION</b></p><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

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