Lead Engineer Applied Generative AI

Role & responsibilities

2. Position Summary

The Lead Engineer Applied Generative AI leads the technical development and delivery of Generative AI solutions for engineering and product development: engineering knowledge systems, Generative Design and selected analytics applications. This is a hands-on technical leadership role framing complex engineering problems, choosing the right AI approach, setting the architecture, writing and reviewing critical code, validating rigorously and taking solutions from prototype to production.

The right candidate is a builder first: a machine-learning or AI engineer whose recent work is Generative AI, LLM and agentic systems running in production, who still codes, and who has grown into technical ownership of real-world solutions. Senior managers, architects who no longer build, and engineers who have added GenAI to a different primary stack are not a fit. Exposure to engineering data, CAD/CAE or simulation is a strong differentiator but can be learnt.

3. Must Have Skills

• Hands-on AI/ML model development — statistical, predictive or deep-learning models built and validated personally
• Generative AI / LLM applications delivered to production — RAG, grounding and retrieval over enterprise documents
• Agentic AI orchestration — multi-step agents with tools/APIs, memory, guardrails and traceability (LangGraph, LangChain, LlamaIndex or equivalent)
• Python and production software engineering — modular APIs and services, containers, CI/CD, MLOps / LLMOps

4. Good To Have Skills

• Adapting foundation and open-weight models: fine-tuning, PEFT / LoRA / QLoRA, smaller models
• AI evaluation and reliability: evaluation datasets, grounding checks, regression testing, drift and fallback handling
• Knowledge intelligence: knowledge graphs, ontologies, metadata and provenance, GraphRAG
• Multimodal models (text, image, drawings, documents)
• Model serving and deployment on GPU and on-premise infrastructure (vLLM, Triton or similar), with judgement on latency, throughput and cost
• Generative design, design-space exploration and optimisation; understanding of geometry, parameters and constraints
• CAD / CAE / simulation workflows and engineering or automotive product data
• Mentoring engineers and setting technical quality standards
• Research publications, patents or a Master's / PhD in AI or a related field

5. Roles and Responsibilities

• Frame engineering problems and select the appropriate approach — statistical, predictive, prognostic, optimisation or generative — then translate results into actionable engineering decisions.
• Select, adapt and evaluate foundation, open-weight, hosted and smaller models using prompting, multimodal methods and fine-tuning where appropriate.
• Design reliable multi-step agentic AI systems with reasoning, planning, context, memory, tool and API use, guardrails, traceability and appropriate human control.
• Extract and structure knowledge from heterogeneous engineering sources; build semantic representations and knowledge graphs with provenance to enable reliable retrieval, grounding and reasoning.
• Apply AI to design generation, design-space exploration and optimisation, working with parameters, geometry, constraints and CAD/CAE/simulation validation.
• Define evaluation and scenario datasets, grounding and evidence checks, failure and regression testing, drift and fallback mechanisms, and human review for dependable AI solutions.
• Architect end-to-end GenAI solutions — models, knowledge, tools, workflows, interfaces and deployment patterns — with security, scalability and maintainability.
• Design, write and review modular, production-quality AI applications, APIs and services.
• Make sound decisions on model serving, containers, CI/CD, MLOps/LLMOps, observability, compute/GPU, latency, cost and on-premise versus cloud deployment.
• Own significant projects end to end, from problem framing through deployment and measurable outcome, while staying hands-on on critical technical problems.
• Set technical direction and quality standards, challenge approaches, resolve difficult trade-offs and mentor engineers and subject-matter experts.
• Build reusable methods, components, evaluation practices and platform assets, and convert relevant emerging AI advances into practical engineering capability.

6. Behavioural Attributes

• Hands-on ownership — stays close to the code and the data on critical problems
• Rigour — validates before claiming results; prefers dependable over impressive
• Curiosity and industry awareness — tracks and tests emerging AI and engineering-AI methods
• Technical mentorship — raises the standard of the engineers around them

Preferred candidate profile