Senior AI Engineer| London

• Role
• AI Evangelist (Senior Technology Architect)
• Technology
• AI/ML/Gen AI, Data Science, Poly Cloud – Azure, AWS, GCP
• Location
• London – UK
• Business Unit
• TOPAZDLVRY

Compensation

• Competitive (including bonus)
• Job Summary

We are seeking an accomplished Generative AI Consultant to drive the design and implementation of innovative AI solutions for our clients.

The Generative AI Consultant will play a critical role in understanding client needs, designing tailored solutions, and ensuring the successful delivery of projects that meet defined metrics.

This role requires strong technical expertise across Generative and Agentic AI—including LLMs, retrieval‑augmented generation (RAG), autonomous and multi‑agent systems, and modern interoperability standards such as the Model Context Protocol (MCP)—coupled with excellent communication skills to engage with clients and internal teams effectively.

• Primary Skill Set
• Generative AI Expertise – understanding of modern Generative AI techniques and foundation models, including transformer‑based Large Language Models (LLMs), diffusion models, and multimodal models, as well as earlier architectures such as GANs and VAEs.

Proven experience applying these techniques to real‑world problems for tasks such as text, code, image, and multimodal generation.

Conversant with modern Gen AI development techniques and tooling such as advanced prompt engineering, structured outputs, function/tool calling, and orchestration frameworks like Lang Chain, Lang Graph, Llama Index, and Semantic Kernel.

Hands‑on exposure to both API‑based (e. g., Claude, GPT, Gemini) and open‑source (e. g., Llama, Mistral) LLM‑based solution design.

• Agentic AI & Orchestration – design of autonomous and multi‑agent systems that reason, plan, and act using tools.

Familiarity with agentic design patterns (e. g., Re Act, planning, reflection, tool use, human‑in‑the‑loop) and agent frameworks such as Lang Graph, Crew AI, MAF, the Open AI Agents SDK, and Google’s Agent Development Kit (ADK).

Experience building agentic workflows with memory, state management, and reliable multi‑step task execution.

• Model Context Protocol (MCP) & Interoperability – practical understanding of the Model Context Protocol for standardized, secure connectivity between LLMs/agents and external tools, data sources, and systems.

Ability to build and consume MCP servers and clients, and to work with MCP primitives such as tools, resources, and prompts.

Awareness of related interoperability standards (e. g., agent‑to‑agent communication) for composing enterprise‑grade agentic systems.

• Agent Skills & Extensibility – experience extending agent capabilities through modular, reusable skills—packaged instructions, scripts, and resources that agents load on demand via progressive disclosure.

Ability to design custom tools, connectors, and skills that let agents perform specialized, domain‑specific tasks reliably and safely.

• Retrieval‑Augmented Generation (RAG) & Knowledge Systems – proven experience designing RAG and knowledge‑grounded systems, including chunking strategies, embeddings, vector databases (e. g., Pinecone, Weaviate, Chroma, pgvector, FAISS), hybrid search, reranking, and evaluation of retrieval quality.

Familiarity with advanced patterns such as Graph RAG and agentic RAG to reduce hallucination and improve factual grounding.

• Technical Proficiency – overall understanding of machine learning algorithms, data science tools, cloud computing platforms, NLP, computer vision, robotics, data ethics, foundation models & LLMs, LLM application & agent frameworks, interoperability & integration, cloud AI platforms & model hosting, vector databases & retrieval, MLOps/LLMOps & deployment, responsible AI & safety, solution design, LLMOps, evaluation & optimization, and communication skills.
• Secondary Skill Set
• Domain Knowledge – familiarity with industry domains and their specific AI challenges.
• Project Management – basic skills to oversee timelines, milestones, and deliverables.
• Data Understanding – foundational grasp of data preprocessing, feature engineering, and data quality assurance.
• Responsible AI & Governance – awareness of AI governance, safety, compliance, data privacy, security, bias and fairness, transparency, and emerging AI regulations.
• Roles & Responsibilities
• Client Interaction – collaborate with business teams to elicit requirements and translate needs into technical requirements and AI solution designs.
• Solution Design – create comprehensive AI solution designs covering architecture, model selection, and data requirements.
• Agentic Solution Architecture – architect Generative and Agentic AI solutions, selecting appropriate frameworks, RAG strategies, MCP integrations, and skills, while defining patterns for reliability, safety, human oversight, and scalable deployment.
• Metrics Definition – work closely with clients to agree on measurable metrics aligned with business goals, and ensure performance evaluation against these metrics.
• Technical Implementation – guide internal teams on implementation, collaborating with data scientists and engineers for effective integration.
• Performance Monitoring – establish mechanisms to monitor deployments, recommend improvements based on observed outcomes.
• Client Collaboration – act as liaison, maintain communication throughout the project life‑cycle, provide updates, and address client concerns.

Qualifications

• High analytical skills.
• High degree of initiative, flexibility, and adaptability.
• High customer orientation.
• Good team engagement skills.
• Quality awareness.
• Good verbal and written communication skills.
• Transparency and integrity.
• Taking accountability.

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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