Senior AI Engineer| London

Role Overview

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: Good 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 tasks (text, code, image, multimodal generation) and familiarity with advanced prompt engineering, structured outputs, function/tool calling, and orchestration frameworks like LangChain, LangGraph, LlamaIndex, and Semantic Kernel. Hands‑on exposure to API-based LLM providers (Claude, GPT, Gemini) and open‑source solutions (Llama, Mistral).
• Agentic AI & Orchestration: Hands‑on experience designing autonomous and multi‑agent systems that reason, plan, and act using tools. Familiarity with agentic design patterns (ReAct, planning, reflection, human‑in‑the‑loop) and agent frameworks such as LangGraph, CrewAI, MAF, OpenAI 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 MCP for standardized, secure connectivity between LLMs/agents and external tools, data sources, and systems. Ability to build and consume MCP servers/clients and work with MCP primitives (tools, resources, prompts). Awareness of related interoperability standards for enterprise‑grade agentic systems.
• Agent Skills & Extensibility: Experience extending agent capabilities through modular, reusable skills—packaged instructions, scripts, and resources (e.g., SKILL.md‑style modules)—that agents load on demand. Ability to design custom tools, connectors, and skills enabling 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 (Pinecone, Weaviate, Chroma, pgvector, FAISS), hybrid search, reranking, and evaluation of retrieval quality. Familiarity with advanced patterns such as GraphRAG and agentic RAG to reduce hallucination and improve factual grounding.

Technical Proficiency

• Machine learning algorithms: linear regression, logistic regression, decision trees, random forests, support vector machines, neural networks
• Data science tools: NumPy, SciPy, Pandas, Matplotlib, TensorFlow, Keras
• Cloud computing platforms: AWS, Azure, GCP
• Natural language processing: transformer models, attention mechanisms, word embeddings
• Computer vision: convolutional neural networks, recurrent neural networks, object detection
• Robotics: reinforcement learning, motion planning, control systems
• Data ethics: bias in machine learning, fairness in algorithms
• Foundation models & LLMs: GPT, Claude, Gemini, Llama, Mistral; multimodal and reasoning models; context windows, tokenization, fine‑tuning (LoRA/PEFT); RLHF/RLAIF concepts
• LLM application & agent frameworks: LangChain, LangGraph, LlamaIndex, Semantic Kernel, Haystack, CrewAI, AutoGen
• Interoperability & integration: MCP, function/tool calling, structured outputs, API integration, event‑driven and orchestration patterns
• Cloud AI platforms & model hosting: Amazon Bedrock, Azure OpenAI / AI Foundry, Google Vertex AI, Hugging Face
• Vector databases & retrieval: Pinecone, Weaviate, Chroma, pgvector, FAISS; embeddings, semantic and hybrid search, reranking
• MLOps / LLMOps & deployment: Docker, Kubernetes, FastAPI, CI/CD; observability, tracing, evaluation tooling (LangSmith, LangFuse); guardrails, prompt/version management
• Responsible AI & safety: bias & fairness, hallucination mitigation, evaluation, privacy, security, governance of AI and agentic systems

Solution Design:

Ability to design end‑to‑end Generative and Agentic AI solutions, from requirement elicitation and model selection to deployment strategy. Experience crafting architectures that encompass data preprocessing, RAG pipelines, agent orchestration, MCP‑based tool and system integration, model integration, guardrails, and performance, cost, and latency optimization.

LLMOps, Evaluation & Optimization:

Experience operationalizing LLM and agentic applications—building evaluation harnesses and offline/online metrics for quality, groundedness, and safety; implementing observability, tracing, and monitoring; continuously optimizing accuracy, cost, and latency. Familiarity with guardrails, red‑team, and responsible deployment of AI systems in production.

Communication Skills:

Excellent verbal and written communication to engage with clients, articulate technical concepts to non‑technical stakeholders, and collaborate with cross‑functional teams.

Secondary Skill Set

• Domain Knowledge: Familiarity with industry domains (healthcare, finance, manufacturing) and specific challenges and requirements of AI solutions in those sectors.
• Project Management: Basic project management skills, overseeing timelines, milestones, and deliverables; coordinating internal teams and clients to ensure project success.
• Data Understanding: Foundational grasp of data preprocessing, feature engineering, and data quality assurance processes to support AI model requirements.
• Responsible AI & Governance: Awareness of AI governance, safety, and compliance considerations—data privacy, security, bias, fairness, transparency, and emerging AI regulations—and their impact on design and deployment.

Roles & Responsibilities

• Client Interaction: Collaborate with client business teams to elicit project requirements and comprehend desired outcomes; translate needs into technical requirements and AI solution designs.
• Solution Design: Create comprehensive AI solution designs addressing client objectives; define architecture, model selection, and data requirements for successful execution.
• Agentic Solution Architecture: Architect Generative and Agentic AI solutions—select appropriate frameworks, RAG strategies, MCP‑based integrations, and skills; define patterns for reliability, safety, human oversight, and scalable production deployment.
• Metrics Definition: Work closely with clients to define and agree on measurable metrics aligned with business goals; ensure AI solution performance is evaluated against these metrics.
• Technical Implementation: Provide guidance to internal teams on implementing the defined AI solution; collaborate with data scientists and engineers to integrate effectively.
• Performance Monitoring: Establish mechanisms to monitor and assess performance of deployed AI models; recommend improvements based on observed outcomes.
• Client Collaboration: Act as liaison between client and internal teams, maintaining effective communication throughout the project lifecycle; provide regular updates and address client queries.

Personal Qualities & Traits

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