Strong proficiency in Python (minimum of 3 years), applying SOLID principles and best practices
Hands-on experience with Azure Cloud and Azure AI Services (Azure OpenAI, Azure AI Search, Azure Functions, APIM)
Experience with RAG frameworks and ingestion pipelines (LangChain, LangGraph, LlamaIndex or equivalent)
Knowledge of vector databases (Azure AI Search, pgvector, Chroma, Pinecone)
Experience with REST APIs and enterprise system integrations (Jira, Confluence, GitHub)
Solid understanding of DevOps and MLOps practices (Docker, Kubernetes, GitHub Actions, Azure DevOps, Terraform)
Experience with identity and access management (Azure AD, OAuth2, RBAC)
Familiarity with observability, monitoring, and performance optimization
Strong problem-solving skills and ability to work in distributed teams
Implement and maintain the LLM gateway (model routing, cost control, failover, rate limiting)
Develop internal SDK components, including agent abstractions, tool contracts, and memory/context interfaces
Build and maintain RAG ingestion pipelines for code repositories, documentation, and knowledge bases
Implement embeddings and vector store solutions with hybrid search (semantic + keyword)
Develop Knowledge Graphs and enterprise adapters (Git, Jira, Confluence)
Implement security controls such as output guardrails, sensitive data masking, RBAC, and prompt injection protection
Configure agent sandboxing and integrate identity systems (Azure AD / OAuth2)
Set up observability frameworks including dashboards, structured logging, and productivity reporting
Implement resilience mechanisms (circuit breakers, manual overrides, Human-in-the-Loop via Teams)
Develop evaluation benchmarks and continuous improvement feedback loops
Integrate CI/CD pipelines for automated deployment of agents (GitHub Actions, ArgoCD)
Integrate platform components with enterprise tools (Jira, Confluence, GitHub, Microsoft Teams)
Manage cloud infrastructure (Azure AI Foundry / AWS Bedrock / Vertex AI) and optimize LLM cost and performance
