GenAI Engineer (LLM Applications on Azure)

Expert-level engineer designing and building LLM applications and agentic workflows on Azure: RAG pipelines, multi-agent orchestration, MCP tool integration, evaluation, guardrails and cost control. Sets the GenAI engineering standards for the account and is accountable for solution quality against agreed evaluation thresholds.

Responsibilities

Build LLM applications and agent workflows with LangGraph / LangChain / Semantic Kernel on Azure OpenAI (AWS Bedrock / GCP Vertex a plus).

Design RAG pipelines: document ingestion, chunking strategies, embeddings, vector search (Azure AI Search, FAISS, Pinecone), hybrid retrieval and re-ranking.

Integrate enterprise tools and context via Model Context Protocol (MCP) servers and function / tool calling; design multi-agent systems.

Engineer, version and test prompts; run offline and online evaluations (accuracy, grounding, hallucination rate) and fine-tuning where justified.

Implement responsible-AI guardrails (content filters, PII redaction, policy checks) and LLM observability (tracing, token accounting, latency).

Optimise token and inference cost (model routing, caching, batching); report against cost budgets.

Document prompts, evaluation results and model choices; mentor engineers adopting GenAI patterns.

Skills

Must have

Bachelor's degree in Computer Science, Engineering, Information Systems or a related field, or equivalent practical experience.

10+ years in software or data engineering, of which 3+ in machine learning / NLP and 2+ delivering production LLM applications (RAG, agents) on Azure OpenAI or equivalent.

Expert Python; LangGraph, LangChain and/or Semantic Kernel; asynchronous, event-driven application design.

Azure OpenAI, Azure AI Search, Azure AI Foundry / ML, Functions, Container Apps / AKS, Key Vault, Application Insights.

RAG design: chunking, embeddings, vector databases (Azure AI Search, FAISS, Pinecone), hybrid search, re-ranking.

MCP server / client development, function calling, multi-agent orchestration and state management.

LLM evaluation (RAGAS, promptfoo, Azure AI evaluation or equivalent), fine-tuning (LoRA / PEFT) and model selection.

Guardrails (Azure AI Content Safety, NeMo Guardrails or similar), LLM observability (LangSmith, OpenTelemetry), FinOps for inference.

Nice to have

AWS Bedrock or GCP Vertex AI.

Semantic caching, model distillation, small-language-model deployment.

Azure AI Engineer Associate (AI-102) or equivalent.

Experience in financial services, sovereign wealth / investment holding or other regulated enterprise environments.

Experience working with distributed teams (onsite UAE with nearshore India / offshore Poland squads).

Other

Languages

English: C1 Advanced

Seniority

Senior