Forward Deployed Engineer - GenAI

About the role

We are looking for a Forward Deployed Engineer to build production‑ready generative AI applications. You will work directly with client engineers to translate business requirements into LLM‑powered features, RAG pipelines, and enterprise search solutions.

Key responsibilities

• Design and implement LLM‑powered features, copilot/chat experiences, and enterprise search.
• Create and optimise RAG pipelines, including chunking, embedding selection, hybrid retrieval, re‑ranking and GraphRAG where needed.
• Fine‑tune and adapt models using LoRA/QLoRA and manage production prompt versions.
• Integrate LLM APIs (OpenAI, Anthropic, Azure OpenAI) and open‑source model endpoints with authentication, rate‑limiting and cost controls.
• Instrument applications for evaluation, logging, quality scoring and human‑feedback loops.
• Optimise latency and token cost through caching, batching and model routing.
• Communicate technical trade‑offs to non‑technical stakeholders and document architecture decisions.

Required profile

• 4–8 years of software engineering experience, including 1–3 years building GenAI/LLM applications.
• Strong Python skills and experience with orchestration frameworks such as LangChain or LlamaIndex.
• Hands‑on experience with vector databases (Pinecone, Weaviate, pgvector) and graph‑database tooling (Neo4j) where relevant.
• Understanding of LLM failure modes and ability to design mitigations.
• Experience delivering solutions directly to client engineers in an embedded setting.

Required skills

• Python
• LangChain, LlamaIndex
• Pinecone, Weaviate, pgvector
• Neo4j
• OpenAI, Anthropic, Azure OpenAI APIs
• LoRA, QLoRA fine‑tuning
• Microsoft Azure AI Foundry, AWS Bedrock, Strands Agents SDK, Google Vertex AI
• API design, authentication, rate limiting, streaming responses
• Prompt‑versioning and LLMOps tools (LangSmith, Weights & Biases)