Azure GenAI Architect for LLM Apps & Multi-Agent Workflows

Tanqeeb; Abu Dhabi; On-site; AED 300,000 - 540,000; Full time

Job summary

Tanqeeb seeks an expert engineer to design and build LLM applications and agent workflows on Azure OpenAI, ensuring high-quality, cost-efficient GenAI solutions with MCP integration and guardrails. You will lead RAG implementations, evaluation, and observability while mentoring teams across nearshore squads.

Requires 10+ years in software/data engineering, 3+ years in ML/NLP and 2+ years delivering production LLM apps (RAG/agents) on Azure OpenAI or equivalent; AWS/GCP experience is a plus.

Qualifications

• Bachelor's degree in Computer Science, Engineering, Information Systems or a related field.
• 10+ years in software or data engineering, including 3+ years in ML/NLP and 2+ years 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 and 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.

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

Python

LangGraph

LangChain

Semantic Kernel

Azure OpenAI

RAG design

MCP protocol

LLM evaluation

Guardrails

Costs optimization

English (C1 Advanced)

Education

Bachelor's degree in CS/Engineering/IS

Tools

LangGraph

LangChain

Semantic Kernel

Azure OpenAI

Azure AI Search

Azure AI Foundry/ML

Azure Functions

Container Apps / AKS

Key Vault

Application Insights

Job description

Tanqeeb seeks an expert engineer to design and build LLM applications and agent workflows on Azure OpenAI, ensuring high-quality, cost-efficient GenAI solutions with MCP integration and guardrails. You will lead RAG implementations, evaluation, and observability while mentoring teams across nearshore squads.

Requires 10+ years in software/data engineering, 3+ years in ML/NLP and 2+ years delivering production LLM apps (RAG/agents) on Azure OpenAI or equivalent; AWS/GCP experience is a plus.