Forward Deployed Engineer - Lead AI & Agentic Engineer

As a Forward Deployed Lead / Principal Engineer (FDE) with an AI & Agentic Engineering focus, you own the technical direction and delivery of major enterprise AI programmes — architecting the agentic systems and generative AI applications that go into production in close collaboration with platform engineering. You align enterprise goals with target-state AI architectures for senior stakeholders, guide the team’s technical direction while staying close to the work, and work hand-in-hand with the platform engineers on the team throughout design, build, and deployment.

You combine deep expertise in agentic AI systems, RAG, and applied software engineering with strong consulting acumen. You establish AI governance frameworks, champion sound engineering practice across the AI delivery lifecycle, and act as a go-to technical reference on complex engagements.

What You Will Do

• Design and build agentic AI applications and multi-agent workflows, along with the frameworks that run them, for enterprise use cases.
• Build RAG pipelines and integrate LLM APIs, vector databases, and MCP (Model Context Protocol) tooling.
• Process unstructured data into condensed, structured knowledge, including ontology extraction.
• Write production-grade Python services (FastAPI) with solid engineering practices: design, testing, code review, CI/CD.
• Work with relational and graph databases (e.g. Postgres, Neo4j) to model and serve data behind AI applications.
• Work closely with platform engineering throughout deployment — hosting, scaling, and MLOps/LLMOps.
• Define AI governance and responsible-use guardrails: data privacy boundaries, LLM governance, and policy enforcement (OPA/Rego).
• Instrument AI applications for observability (OpenTelemetry, Prometheus) so behaviour and cost stay visible in production.
• Translate enterprise requirements into AI solution roadmaps for senior stakeholders.
• Capture field learnings, codify reusable agentic patterns, and mentor engineers hands-on.
• Provide architectural oversight across multi-disciplinary workstreams, staying close enough to unblock the team directly.

You don’t need to tick every box below to apply — this reflects the breadth of the role, not a strict checklist.

Required Skills and Experience

• 8–10+ years in software or solution engineering, with a track record of shipping AI systems in client-facing engagements.
• Strong Python engineering (FastAPI), with solid SDLC practices: design, testing, code review, CI/CD, Git & GitHub.
• Hands‑on experience with agentic AI frameworks (examples include LangGraph, CrewAI, AutoGen, Semantic Kernel, OpenAI’s Agents SDK, and Google’s Agent Development Kit) and MCP (Model Context Protocol).
• Practical experience building RAG/LLM-based pipelines and working with vector databases.
• Experience with relational and graph databases (e.g. Postgres, Neo4j) for data modelling behind AI applications.
• Hands‑on familiarity with modern AI coding copilots (e.g. Claude, Codex, Cursor).
• Strong stakeholder communication, translating AI capability into business outcomes for senior stakeholders.

Preferred Skills and Experience

• Familiarity with observability instrumentation for AI systems (OpenTelemetry, Prometheus).
• Experience with AI/ML frameworks (TensorFlow, PyTorch) and the Hugging Face / open-source AI ecosystem.
• Experience with image understanding and OCR.
• Working knowledge of policy-as-code (OPA/Rego) for AI governance and guardrails.
• Understanding of LLM governance: data privacy, guardrails, and responsible-use controls.
• T‑shaped profile: deep expertise in AI engineering, broad understanding across software engineering and technical consulting.
• Willingness to travel and work on customer premises as required.
• Degree in Computer Science, Data Science, Informatics, Engineering, Physics, Mathematics, or a related discipline — or equivalent professional experience.