AI/ML Engineer

Key Responsibilities Strong hands‑on experience, understanding of modern AI/ML technologies, generative AI frameworks including LangChain, LangGraph, and Retrieval‑Augmented Generation (RAG), and extensive experience designing and implementing agentic AI workflows and multi‑agent systems. 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), Socket.IO, Redis, and AWS SageMaker. 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 Socket.IO for seamless, low‑latency user interactions with frontend applications (e.g., Angular, React). 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. 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. #J-18808-Ljbffr