Description

Design and operate Retrieval-Augmented Generation (RAG) services and agentic AI solutions on Azure to automate workflows and enhance security.

This role is hybrid.

Responsibilities

• Implement RAG pipelines using Azure AI/Search, including chunking, embeddings, re-ranking, and citation display.
• Build agentic AI systems with AutoGen, CrewAI, or Agno for secure multi-agent orchestration and tool-calling.
• Develop production-grade chatbots with prompt management, guardrails, audit logging, and telemetry.
• Deploy Model Context Protocol (MCP) tools to integrate security scanners, inventory systems, and Azure DevOps/GitHub.
• Ship via CI/CD on Azure DevOps, ensuring unit/integration tests, red-teaming for prompt injection, and runbook documentation.

Required Skills

• 4-8 years total software development experience, with 2+ years in applied LLM/GenAI.
• Strong Python skills and hands-on experience with Azure OpenAI and Azure AI/Search (vector/hybrid search).
• Practical experience with agent frameworks (AutoGen, CrewAI, Agno) and MCP/tool-use patterns.
• Proven Azure PaaS experience: Azure Functions/Web Apps, APIM, Key Vault, Event Hub, and Entra ID/RBAC.
• Experience implementing observability (App Insights, Log Analytics/KQL) and CI/CD with Azure DevOps.
• Evaluate and fine-tune open-source models (PEFT/LoRA) balancing quality, latency, cost, and safety.
• Integrate Azure OpenAI behind API Management, managing secrets and events securely.

Preferred Skills

• RAG evaluation frameworks (e.g., Ragas), custom golden sets, and Cosmos DB familiarity.
• Security-first mindset: content safety, prompt-injection defenses, data privacy, and threat modeling for AI.
• Experience with cost monitoring/optimization of LLM workloads and latency tuning.

Key Skills

Education

Any Graduate