Your role
Backend & Multi-Agent AI Services
Build and maintain backend services using Azure Functions, Semantic Kernel, LangChain/LangGraph/AutoGen/CrewAI.
Develop APIs to orchestrate LLM workflows, manage state, and support chat and analytics front ends.
Implement multi-agent pipelines with planning, reasoning, and execution flows.
Optimize performance, latency, and token usage with robust error handling and scalable design.
RAG, Data, & AI Pipelines
Design and manage RAG pipelines (chunking, embedding, indexing, hybrid retrieval).
Extend retrieval with GraphRAG and entity-driven reasoning.
Build ingestion pipelines to validate, clean, and test data before adding it to the AI knowledge layer.
Integrate data from CosmosDB, Dataverse, APIs, Azure Cognitive Search, SharePoint, and other enterprise sources.
Create automated evaluation methods for retrieval accuracy, hallucination, and dataset quality.
Full Stack Integration (React/
Collaborate with front-end engineers to define API contracts, payloads, and session flows.
Support full-stack applications by providing reusable backend services, SDKs, and documentation.
Align backend services with React, , and shadcn frameworks for modern enterprise UIs.
Observability, Security & Governance
Implement telemetry for latency, retrieval hit/miss, hallucination, cost, and user engagement.
Integrate logging and metrics with observability tools (OpenTelemetry, Prometheus, Grafana).
Enforce RBAC, compliance, and secure handling of enterprise data, including masking, sanitization, and lineage tracking.
What you’ll need
Core Expertise
Strong hands-on experience with Azure AI/data stack: Cognitive Search, Azure OpenAI, CosmosDB, Azure Functions, AKS, Dataverse.
Proficiency in Python and backend frameworks (FastAPI/Flask/Django).
Experience with RAG pipelines, multi-agent frameworks (LangChain, LangGraph, AutoGen, CrewAI), and Semantic Kernel.
Familiarity with vector stores (FAISS, Pinecone, Weaviate, Azure Cognitive Search).
Working knowledge of React/ to support API integration with UIs.
Experience
4+ years in backend, ML infrastructure, or cloud engineering.
2+ years building AI/LLM-based applications, ideally in enterprise environments.
Proven experience integrating multiple enterprise data sources securely.
Background in data quality testing, validation, and continuous evaluation for AI systems.
Exposure to full-stack development with React/.
Nice-to-Haves
Experience with GraphRAG, knowledge graphs, and entity-based retrieval.
Experience with continuous LLM evaluation (DeepEval, G-Eval, custom pipelines).
End-to-end experience building full-stack AI solutions (backend + React/ front end).