Ai Data Engineer - 17852

AI Engineer (Backend) – PythonKey ResponsibilitiesDevelopment- Design and develop Python backend services using FastAPI FastAPI for AI-poweredapplications- Implement agentic workflows using LangChain LangChain and / or Azure AI agenticframeworks like AutoGen Azure AI agentic frameworks like AutoGen- Build and maintain RAG systems for context-aware AI responses- Develop MCP servers and integrations for tool calling and external API interactions- Create sandboxed execution environments for AI-generated code and visualizationsData & SQL- Write efficient SQL queries against data lakehouses to retrieve business insights- Integrate with Tableau dashboards and other BI platforms- Optimize data retrieval patterns for LLM context windowsAI / ML Operations- Build benchmarking systems to evaluate LLM performance across different models andproviders Implement model switching logic to automatically use the most efficient / effectiveLLM- Monitor and optimize token usage, costs, and response latency- Work with AWS Bedrock and Azure AI Foundry for model deploymentRequired QualificationsTechnical Skills- 3+ years of professional Python development experience- Strong SQL skills with experience querying databases or data lakehouses- Experience with FastAPI or similar Python web frameworks (Flask, Django)- Hands-on experience with AWS Bedrock or Azure AI services- Familiarity with Docker containerization- Experience with Kubernetes (K8s) deployments- Proficiency with Git and GitLab CI / CD or similar CI / CD tools- Understanding of RESTful API design and developmentAI / ML Experience- Working knowledge of LLM technologies (GPT, Claude, Llama, etc.)- Experience with prompt engineering and LLM application development- Familiarity with LangChain, AutoGen, Azure AI frameworks, or similar agentic / orchestrationframeworks Understanding of RAG (Retrieval-Augmented Generation) patternsPreferred Qualifications- Experience with React or frontend development (bonus, not required)- Familiarity with Tableau or other BI platforms- Experience with data lakehouses (Databricks, Snowflake, etc.)- Knowledge of Model Context Protocol (MCP) or similar tool-calling frameworks- Experience building benchmarking or evaluation systems for ML models- Background in financial services or marketing analytics- Contributions to open-source AI / ML projects