Gen AI Engineer

Mandatory Skills

Strong hands-on experience understanding of modern AI/ML technologies Generative AI frameworks including LangChain LangGraph and Retrieval-Augmented Generation (RAG) and extensive experience in designing and implementing agentic AI workflows and multi-agent systems

JD

Key Responsibilities

• • instrumental in architecting and deploying production-grade AI solutions using Azure OpenAI (GPT-4o) Azure Document Intelligence and serverless computing paradigms on Microsoft Azure
  • Developing and designing solutions using Python FastAPI LangChain LangGraph Azure OpenAI (GPT-4o) Azure Document Intelligence Azure Functions Azure Blob Storage Snowflake MongoDB (Vector Search) SQL Docker MLflow GitHub Actions (CI/CD) Redis and AWS SageMaker

o Backend Development

• • Build and maintain robust production-grade backend APIs using FastAPI or Flask ensuring secure authentication input validation and structured error handling.
  • Implement secure event-driven data pipelines (e.g. Azure Functions) to automate extraction transformation and loading of structured and unstructured data across cloud storage and data warehouses (Azure Blob Storage Snowflake).
  • Manage database integrations including SQL databases Snowflake and MongoDB (Vector Search) to support both transactional and AI-driven retrieval workflows.
  • Optimize backend systems for real-time processing of AI queries and responses implementing asynchronous Python patterns and Redis caching to minimize latency under concurrent load.
  • Integrate real-time communication frameworks such as for seamless low-latency user interactions with frontend applications (e.g. Angular React).

2. Generative AI Model Integration

• • Utilize Azure OpenAI (GPT-4o) and related services to build LLM-powered applications including Retrieval-Augmented Generation (RAG) systems with hybrid search (keyword semantic).
  • Architect and orchestrate multi-agent systems using LangChain and LangGraph designing specialized agents for tasks such as content generation intelligent data extraction and automated decision-making.
  • Deploy fine-tune and integrate AI models into business applications working closely with product and business stakeholders to align model outputs with business objectives.
  • Optimize AI-driven prompt engineering and embedding models for efficient performance iterating on system prompts chunking strategies and retrieval pipelines to maximize accuracy and reduce API costs.
  • Leverage Azure Document Intelligence for parsing unstructured documents (PDFs earnings reports) and extracting structured financial or operational KPIs at scale.
  • Build and maintain Model Context Protocol (MCP) servers to expose internal databases and documentation to LLM clients for secure standardized data retrieval.

3. Containerization & Deployment

• • Use Docker to containerize AI applications and their dependencies ensuring consistent behavior across development staging and production environments.
  • Manage end-to-end application deployments in Azure environments (Azure Functions Azure Workspace Azure Blob Storage) including infrastructure setup and configuration.
  • Engineer CI/CD pipelines using GitHub Actions to automate testing building and deployment processes for seamless zero-downtime releases.
  • Monitor troubleshoot and resolve application performance issues post-deployment using MLflow custom dashboards automated alerts and logging systems.
  • Implement model monitoring practices to detect data drift performance degradation and data quality issues in production ML/AI systems.