Senior AI Architect| London

Role

AI Evangelist (Senior Technology Architect)

Technology, Location & Compensation

AI/ML/Gen AI, Data Science, Poly Cloud – Azure, AWS, GCPLondon, UKCompetitive (including bonus)

Job Summary

We seek a highly skilled senior architect/consultant to lead the Generative AI Technologies team. The candidate must possess deep expertise in Generative and Agentic AI, including LLMs, retrieval-augmented generation (RAG), machine learning, and interoperability standards such as the Model Context Protocol (MCP). The role involves shaping the Generative AI strategy, selecting suitable models and technologies, and collaborating with cross‑functional teams to deliver customer‑centric, enterprise‑scale solutions.

Primary Skill Set

• Generative AI Expertise – in‑depth knowledge of transformer‑based LLMs, diffusion and multimodal models, and earlier architectures such as GANs and VAEs. Experience across text, code, image, and multimodal generation; advanced prompt engineering; orchestration frameworks (LangChain, LangGraph, LlamaIndex, Semantic Kernel). Hands‑on exposure to both API‑based (Claude, GPT, Gemini) and open‑source (Llama, Mistral) LLM‑based solution design.
• Agentic AI & Multi‑Agent Architecture – design of autonomous and multi‑agent systems, agentic design patterns (ReAct, planning, reflection, tool use, human‑in‑the‑loop), and frameworks (LangGraph, CrewAI, MAF, OpenAI Agents SDK, Google ADK). Proven ability to architect reliable agentic workflows with memory, state management and safe multi‑step task execution at scale.
• Model Context Protocol (MCP) & Interoperability – architectural knowledge of MCP for secure connectivity between LLMs/agents and enterprise tools, data sources, and systems; ability to build and govern MCP servers and clients, and familiarity with related interoperability standards.
• Agent Skills & Extensibility – extending agent capabilities through modular, reusable skills and resources; defining standards for custom tools, connectors and skills ensuring reliable, secure, and consistent operation.
• Retrieval‑Augmented Generation (RAG) & Knowledge Architecture – expertise in RAG and knowledge‑grounded systems, including chunking, embeddings, vector databases (Pinecone, Weaviate, Chroma, pgvector, FAISS), hybrid search and retrieval evaluation. Familiarity with GraphRAG and agentic RAG.
• LLMOps, Evaluation & Responsible AI – operationalizing LLM and agentic systems at scale; evaluation harnesses, quality metrics, observability, tracing and monitoring (LangSmith, LangFuse); guardrails, red‑teaming and continuous optimization of accuracy, cost and latency. Understanding of AI governance, security, privacy, bias/fairness and emerging regulations.
• Machine Learning Mastery – profound understanding of ML principles, algorithms, frameworks, and training pipelines.
• Technical Proficiency – programming in Python, TensorFlow, PyTorch or similar; modern LLM/agent frameworks; cloud AI platforms (Amazon Bedrock, Azure OpenAI / AI Foundry, Google Vertex AI); vector databases; containerization and orchestration (Docker, Kubernetes); distributed computing.
• Architecture Design – end‑to‑end design of Generative and Agentic AI architectures encompassing data preprocessing, model selection, RAG pipelines, agent orchestration, MCP‑based integration, guardrails, training/inference pipelines and deployment strategies.

Secondary Skill Set

• Domain Knowledge – familiarity with the specific industry domain (e.g., healthcare, finance, entertainment) to enable contextual and tailored solutions.
• Data Engineering – understanding of data pipelines, preprocessing, cleansing and transformation for model training.
• AI Governance, Security & Responsible AI – knowledge of governance, safety, compliance considerations and emerging regulations impacting architecture and deployment.
• Communication Skills – excellence in conveying complex technical concepts to non‑technical stakeholders and collaborating with cross‑functional teams.

Roles & Responsibilities

• Lead the development of the Generative and Agentic AI technology roadmap, identifying opportunities and proposing innovative, agent‑driven solutions aligned with business goals.
• Evaluate and select appropriate models, agent frameworks, RAG strategies and integration standards (including MCP) based on project requirements, data availability, safety, cost, latency and compute resources.
• Design comprehensive and scalable architectures for Generative AI solutions, covering data preprocessing, model training, deployment and monitoring.
• Define reusable architecture patterns and platform standards for agentic AI, including orchestration, MCP‑based integration, shared skills, memory management, guardrails, human oversight and observability.
• Collaborate with data scientists and engineers to implement Generative AI solutions and integrate models into production environments.
• Continuously optimize performance of Generative AI models, addressing speed, accuracy and resource utilization.
• Assess outcomes against success criteria, iterate on models and strategies based on performance metrics and feedback.
• Work closely with customer architecture and business teams to define requirements, technical boundaries and SLAs, tailoring solutions to customer needs.
• Provide guidance and mentorship to junior team members, fostering a collaborative and innovative work environment.
• Stay updated on the evolving Generative and Agentic AI landscape, sharing insights and incorporating emerging trends into solution and platform architecture.

Personal Traits

• High analytical skills
• Strong initiative, flexibility and adaptability
• High customer orientation
• Quality awareness
• Good verbal and written communication
• Transparency and integrity
• Accountability

Equal Employment Opportunity Statement

All aspects of employment at Infosys are based on merit, competence and performance. We are committed to embracing diversity and creating an inclusive environment for all employees. Infosys is proud to be an equal opportunity employer.

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