GenAI Engineer (LLM Applications on Azure)

Tanqeeb; Abu Dhabi; On-site; AED 480,000 - 720,000; Full time

Job summary

Tanqeeb in the United Arab Emirates seeks an expert-level engineer to design and build LLM applications and agent workflows on Azure OpenAI. You will shape RAG pipelines, multi-agent orchestration, MCP tool integration, evaluation, guardrails and cost control.

Responsibilities include engineering, versioning and testing prompts; running offline and online evaluations and mentoring other engineers adopting GenAI patterns. Strong Azure OpenAI experience is preferred.

Qualifications

• Bachelor's degree or equivalent practical experience in CS/Engineering/IS or related field.
• 10+ years in software or data engineering, with 3+ years in ML/NLP and 2+ delivering production LLM applications on Azure OpenAI or equivalent.
• Expert Python programming and experience with LangGraph, LangChain or Semantic Kernel.
• Hands-on experience with Azure AI services (OpenAI, Search, Foundry/ML), Functions, AKS, Key Vault, App Insights.
• Knowledge of guardrails, observability (LangSmith/OpenTelemetry) and FinOps for inference.

Responsibilities

• Build LLM applications and agent workflows on Azure OpenAI.
• Design RAG pipelines including document ingestion, embeddings, vector search and re-ranking.
• Integrate MCP servers and multi-agent orchestration; implement function calling and tool use.
• Evaluate prompts and models; run offline/online tests and fine-tuning where justified.
• Implement guardrails and observability; optimize token and inference costs.
• Document prompts, results and model choices; mentor other engineers.

Skills

Python

LLM applications

NLP

Distributed systems

Mentoring

Education

Bachelor's degree or equivalent

Tools

LangGraph

LangChain

Semantic Kernel

Azure OpenAI

Azure AI Search

FAISS

Pinecone

MCP

Azure Functions

AKS

Job description

Job description

Project descriptionExpert-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.ResponsibilitiesBuild 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.SkillsMust haveBachelor'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 haveAWS 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).OtherLanguagesEnglish: C1 AdvancedSenioritySenior