{"id":2852,"date":"2026-07-16T19:03:03","date_gmt":"2026-07-16T19:03:03","guid":{"rendered":"https:\/\/coderseditor.com\/itjobs\/job\/senior-ai-architect-london\/"},"modified":"2026-07-16T19:03:03","modified_gmt":"2026-07-16T19:03:03","slug":"senior-ai-architect-london","status":"publish","type":"job_listing","link":"https:\/\/coderseditor.com\/itjobs\/job\/senior-ai-architect-london\/","title":{"rendered":"Senior AI Architect London"},"content":{"rendered":"

Senior AI Architect London Overview<\/h2>\n\n\n\n\n\n\n\n\n
Company Name<\/strong><\/td>\nInfosys<\/td>\n<\/tr>\n
Job Role<\/strong><\/td>\nSenior AI Architect London<\/td>\n<\/tr>\n
Qualifications<\/strong><\/td>\nNot Specified<\/td>\n<\/tr>\n
Category<\/strong><\/td>\nIT Jobs<\/td>\n<\/tr>\n
Job Type<\/strong><\/td>\nFull Time<\/td>\n<\/tr>\n
Location<\/strong><\/td>\nLondon<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

This position is for a senior AI architecture specialist focused on generative and agentic AI delivery in London. The role sits within Infosys\u00e2\u0080\u0099 Topaz Centre of Excellence and is aimed at someone who can act as an AI evangelist and senior technology architect, helping shape how the organisation designs, governs, and deploys advanced AI solutions for enterprise customers.<\/p>\n

The successful candidate will lead the technical direction for generative AI and agent-based systems, working across strategy, architecture, implementation, optimisation, and stakeholder engagement. The role calls for someone who can combine deep technical expertise with the ability to influence customers, guide internal teams, and translate emerging AI capabilities into practical business value.<\/p>\n

What you will do<\/h3>\n
    \n
  • Define the overall direction for generative AI and agentic AI work, including spotting opportunities, selecting promising use cases, and building a roadmap that supports business objectives.<\/li>\n
  • Evaluate and choose suitable models, agent frameworks, retrieval methods, and interoperability standards such as MCP, balancing factors like data availability, complexity, safety, cost, latency, and available compute.<\/li>\n
  • Design complete, scalable AI architectures that cover data preparation, model training, deployment, monitoring, and ongoing support.<\/li>\n
  • Establish reusable architecture patterns and platform standards for agentic systems, including orchestration, tool and data integration through MCP, shared skills and connectors, memory and state handling, guardrails, human review, and observability.<\/li>\n
  • Work closely with data scientists and engineers to implement solutions and move them into production environments without friction.<\/li>\n
  • Continuously tune systems to improve speed, accuracy, and resource efficiency.<\/li>\n
  • Review solution outcomes against agreed success measures and refine models, workflows, and architecture based on performance data and feedback.<\/li>\n
  • Collaborate with customer architecture and business teams to define requirements, technical boundaries, and service expectations, then adapt solutions to fit those needs.<\/li>\n
  • Support and mentor junior colleagues while helping create a collaborative and innovative team environment.<\/li>\n
  • Stay current with developments in generative AI, agentic AI, MCP, skills-based extensibility, and related technologies, and feed those insights into team and platform decisions.<\/li>\n<\/ul>\n

    Technical expertise expected<\/h3>\n
      \n
    • Strong command of modern generative AI, including transformer-based large language models, diffusion models, multimodal models, and earlier approaches such as GANs and VAEs.<\/li>\n
    • Experience delivering solutions across text, code, image, and multimodal generation scenarios.<\/li>\n
    • Practical familiarity with current GenAI development approaches such as advanced prompt engineering, structured outputs, function and tool calling, and orchestration frameworks like LangChain, LangGraph, LlamaIndex, and Semantic Kernel.<\/li>\n
    • Experience designing with both hosted model APIs such as Claude, GPT, and Gemini, and open-source model families such as Llama and Mistral.<\/li>\n
    • Deep knowledge of autonomous and multi-agent systems, including reasoning, planning, action execution, and tool use.<\/li>\n
    • Understanding of agent design patterns such as ReAct, planning, reflection, tool use, and human-in-the-loop control.<\/li>\n
    • Experience with agent frameworks including LangGraph, CrewAI, MAF, the OpenAI Agents SDK, and Google\u00e2\u0080\u0099s Agent Development Kit.<\/li>\n
    • Ability to build reliable agent workflows with memory, state management, orchestration, and safe multi-step execution at scale.<\/li>\n
    • Working knowledge of the Model Context Protocol and how it supports secure, standardised connections between agents or LLMs and enterprise tools, data sources, and systems.<\/li>\n
    • Ability to design, build, and govern MCP servers and clients, and to work with MCP tools, resources, and prompts.<\/li>\n
    • Awareness of related interoperability standards, including agent-to-agent communication, for building scalable enterprise ecosystems.<\/li>\n
    • Experience extending agent capability through modular skills, reusable instructions, scripts, and resources that can be loaded as needed.<\/li>\n
    • Ability to define standards for custom tools, connectors, and skills so that agents can perform specialised tasks consistently and securely across teams.<\/li>\n
    • Strong background in retrieval-augmented generation and knowledge architecture, including chunking, embeddings, vector databases, hybrid search, reranking, and retrieval evaluation.<\/li>\n
    • Familiarity with advanced retrieval approaches such as GraphRAG and agentic RAG to improve grounding and reduce hallucinations in production.<\/li>\n
    • Experience operating LLM and agentic systems at scale, including evaluation harnesses, quality and safety metrics, observability, tracing, monitoring, guardrails, red-teaming, and continuous optimisation of accuracy, cost, and latency.<\/li>\n
    • Understanding of AI governance, security, privacy, bias, fairness, and emerging regulation.<\/li>\n
    • Strong machine learning knowledge, including algorithms, model design, performance tuning, and training pipelines.<\/li>\n
    • Programming ability in AI-relevant languages and tools such as Python, TensorFlow, and PyTorch.<\/li>\n
    • Experience with cloud AI services such as Amazon Bedrock, Azure OpenAI \/ AI Foundry, and Google Vertex AI.<\/li>\n
    • Experience with vector databases such as Pinecone, Weaviate, Chroma, pgvector, and FAISS.<\/li>\n
    • Knowledge of containerisation and orchestration tools such as Docker and Kubernetes, plus distributed computing concepts.<\/li>\n
    • Ability to design full enterprise-grade GenAI and agentic architectures spanning preprocessing, model choice, RAG, orchestration, MCP integration, guardrails, training and inference, and deployment.<\/li>\n
    • Capability to build systems that are scalable, dependable, secure, and efficient in terms of cost and latency.<\/li>\n<\/ul>\n

      Additional experience and personal qualities<\/h3>\n
        \n
      • Familiarity with a relevant industry domain, such as healthcare, finance, or entertainment, would be an advantage.<\/li>\n
      • Understanding of data engineering practices, including pipelines, data management, cleansing, and transformation for model training.<\/li>\n
      • Awareness of governance, safety, compliance, privacy, transparency, auditability, and regulatory issues affecting AI solutions.<\/li>\n
      • Excellent communication and collaboration skills, with the ability to work effectively with data scientists, engineers, business stakeholders, and customers, and to explain complex technical ideas to non-technical audiences.<\/li>\n
      • The employer also values strong analytical thinking, initiative, flexibility, adaptability, customer orientation, teamwork, quality focus, clear verbal and written communication, transparency, integrity, and accountability.<\/li>\n<\/ul>\n

        About the team and organisation<\/h3>\n

        The role is part of Topaz CoE, Infosys\u00e2\u0080\u0099 central unit for AI and automation evangelisation. The group\u00e2\u0080\u0099s purpose is to help the company deliver greater value to customers through AI-led differentiation across horizontal and vertical services, using both internal capabilities and partner ecosystems. It supports Infosys\u00e2\u0080\u0099 ambition to be an AI-first organisation and to strengthen its position as a market leader in AI and generative AI.<\/p>\n

        Topaz helps clients define roadmaps and realise productivity gains and business benefits through automation, AI, and generative AI. The organisation says its AI-first services, solutions, and platforms are designed to amplify human and enterprise potential through innovation, efficiency, and connected ecosystems. It also highlights a large internal AI capability base, including thousands of AI assets, more than 150 pre-trained models, and over 10 AI platforms, all supported by specialists and data strategists and guided by a responsible-by-design approach focused on ethics, trust, privacy, security, and compliance.<\/p>\n

        Infosys describes itself as a global leader in digital services and consulting, supporting clients in many countries as they navigate digital transformation with cloud and AI. The company says it is committed to inclusive hiring, merit-based employment, environmental sustainability, and a workplace where diverse talent can thrive.<\/p>\n

        Compensation and application<\/h3>\n

        The compensation is described as competitive and includes bonus potential. The page provides an application option through the company careers site, where candidates can apply now for the role.<\/p>\n

        No visa sponsorship information is stated on the page.<\/p>\n\n

        Degree Requirement:<\/strong> Not Specified<\/p>\n

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