AI Engineer — AI Advisory & Technology Strategy
Role Title
AI Engineer (Agentic AI & Applied AI Engineering)
Practice
AI Advisory & Technology Strategy, Wipro Consulting
Base Location
London, United Kingdom
Role Type
Permanent, hands-on engineering role within a client-facing advisory practice
Reports To
Partner - AI Advisory & Technology Strategy
Experience
5 to 8 years in software / data / AI engineering, including 2+ years hands-on with Generative AI and Agentic AI
Role Summary
Wipro Consulting's AI Advisory & Technology Strategy practice helps global enterprises move from AI ambition to measurable, production-grade outcomes. We are seeking a hands-on AI Engineer to join the practice in London and act as the engineering spine of our advisory engagements - turning strategy, architecture and use-case hypotheses into working agentic AI systems that clients can see, test and scale.
This is a build-first role. You will spend most of your time writing code: prototyping agentic workflows in executive labs, engineering retrieval and orchestration pipelines, hardening evaluation and guardrail layers, and industrializing successful pilots into scaled delivery. Equally, you will sit alongside client architects and business leaders to shape use cases, assess feasibility, size value and defend design decisions - so the ability to communicate technical choices in business terms is essential.
The ideal candidate is a strong engineer with deep, current command of the emerging agentic AI toolchain, credible industry certifications, and a demonstrable portfolio of AI systems they have personally built and deployed.
Key Responsibilities
Candidate must be able to demonstrate experience in the following AI forte:
Hands-On Agentic AI Engineering
- Design, code, test and deploy multi-agent and single-agent systems using any or few of these frameworks - LangGraph, Microsoft AutoGen, Microsoft Copilot studio, Microsoft Power platform, Semantic Kernel, CrewAI, OpenAI Agents SDK and Google ADK.
- Build and expose tools and connectors using Model Context Protocol (MCP), Agent-to-Agent (A2A) interaction patterns, and function-calling interfaces against enterprise systems.
- Engineer production-grade RAG and Agentic RAG pipelines: chunking and embedding strategies, hybrid and semantic retrieval, re-ranking, grounding, citation and context-window optimization.
- Develop supporting services and APIs (Python/FastAPI, TypeScript/Node.js) with proper testing, CI/CD, containerization and infrastructure-as-code.
- Implement agent orchestration patterns — planner/executor, supervisor-worker, reflection, human-in-the-loop — with tool calling, structured outputs, state management and durable execution.
Use Case Definition, Feasibility and Implementation
- Work with clients to identify, shape and prioritize AI and agentic use cases, assessing technical feasibility, data readiness, effort, cost, and expected business value.
- Translate prioritized use cases into solution designs, reference architectures, backlogs and implementation roadmaps that delivery teams can execute against.
- Build rapid, credible prototypes and demonstrators for executive labs, value sprints and proof-of-value engagements, typically within 2 to 6 week timeboxes.
- Support the industrialization of validated prototypes into scaled delivery, including handover, documentation, and engineering enablement.
- Contribute reusable accelerators, patterns and reference implementations back into the practice asset library.
Evaluation, Guardrails and Responsible AI
- Design and implement evaluation harnesses measuring relevance, grounded-ness, factuality, robustness, safety and task completion (e.g. Ragas, LangSmith, DeepEval, custom harnesses).
- Implement guardrails, prompt-injection defenses, adversarial and bias testing, content filtering and policy enforcement across agentic workflows.
- Instrument systems for observability, cost and token analytics, latency profiling, tracing and drift detection; operate within defined budget guardrails.
- Apply responsible AI practice aligned to the EU AI Act, NIST AI RMF and ISO/IEC 42001, working with our AI governance and risk colleagues.
Client Engagement and Practice Contribution
- Act as the credible hands-on technical voice in client workshops, architecture design sessions, technology evaluations and platform selections.
- Produce high-quality technical documentation, architectural blueprints, demo scripts and enablement material for client and internal teams.
- Support pre-sales and pursuits activity through solution shaping, effort estimation, technical narrative and live demonstrations.
- Coach and mentor junior engineers and consultants; contribute to practice capability building, internal skilling and knowledge assets.
Required Skills and Experience
- Agentic AI: Demonstrable, recent hands-on delivery using at least two agentic frameworks - LangGraph, AutoGen, Semantic Kernel, CrewAI, OpenAI Agents SDK, Google ADK, MS Copilot studio /Power platform including multi-agent orchestration, tool use and MCP.
- Generative AI: Deep practical experience with LLM APIs with proficiency in one or more of Azure OpenAI, OpenAI, Anthropic Claude, Google Gemini, prompt and context engineering, function calling, structured outputs, fine-tuning and model selection trade-offs.
- Programming: Expert-level Python. Strong grounding in software engineering fundamentals - clean code, testing, version control, code review, packaging and deployment. Working proficiency in at least one of TypeScript/JavaScript, Java or C#.
- RAG and Data: Production experience with vector and hybrid search in one or more of - Azure AI Search, Pinecone, Chroma, Elasticsearch — plus embedding, chunking and knowledge-graph approaches (Neo4j).
- Cloud and MLOps: Hands-on delivery on Azure, AWS or GCP AI stacks; Docker, Kubernetes, Terraform, GitHub Actions; MLflow or equivalent for experiment tracking, versioning and serving.
- Consulting Skills: Ability to shape use cases, run technical workshops, articulate trade-offs to non-technical stakeholders and work fluently in client-facing, multi-disciplinary teams.
Education
Bachelor's or Master’s degree in computer science, Engineering, Mathematics, Statistics or a related discipline.
Certifications
Candidates are expected to hold current, verifiable industry certifications. At least one cloud AI certification is mandatory; further certifications are strongly valued.
Certifications
Mandatory (one or more or Equivalent)
Microsoft Certified: Azure AI Engineer Associate (AI-102);
AWS Certified Machine Learning - Specialty or AWS Certified AI Practitioner; Google Cloud Professional Machine Learning Engineer
