Azure AI Data Engineer

Project description

Designed and developed an enterprise-grade Generative AI platform using Python, FastAPI, and FastMCP to automate recruitment workflows and provide intelligent candidate insights. Built scalable, production-ready APIs that integrated with OpenAI GPT and Anthropic Claude models for candidate screening, resume analysis, job matching, and automated recruiter assistance. Implemented RAG (Retrieval-Augmented Generation) pipelines using vector databases such as Pinecone/Milvus to provide context-aware responses from enterprise knowledge repositories. Developed AI Agents and custom plugins/skills to automate candidate engagement, interview scheduling, talent recommendations, and workflow orchestration. Owned the complete application lifecycle, including architecture design, API development, deployment, monitoring, observability, logging, and performance optimization. Leveraged AI-assisted development tools such as Claude, Codex, and VS Code to accelerate development, debugging, code reviews, and delivery.

Responsibilities

• Design, develop, and maintain scalable, high-performance applications using Python, FastAPI, and FastMCP. Build and deploy production-grade REST APIs and AI-powered services following best practices for security, performance, and reliability. Develop and integrate Generative AI solutions using Large Language Models (LLMs) such as OpenAI GPT and Anthropic Claude. Design and implement AI Agents, Plugins, Skills, and MCP-based integrations to automate business workflows. Build and optimize RAG (Retrieval-Augmented Generation) pipelines using vector databases such as Pinecone or Milvus. Create prompt engineering frameworks and context-management strategies to improve LLM response quality. Develop end-to-end AI solutions covering architecture, implementation, testing, deployment, and production support. Collaborate with product managers, architects, and business stakeholders to translate requirements into technical solutions. Implement monitoring, logging, observability, and performance optimization for AI applications and APIs. Ensure code quality through unit testing, code reviews, CI/CD pipelines, and adherence to software engineering best practices. Evaluate and adopt emerging AI technologies, frameworks, and industry best practices. Leverage AI-assisted development tools such as Claude, Codex, and VS Code to accelerate development, debugging, and delivery. Troubleshoot complex technical issues and provide timely resolutions in production environments. Mentor team members and contribute to technical design discussions and innovation initiatives.

SKILLS

Must have

• 7+ years of experience Microservices Development | Python | FastAPI (With Async Programming) | Any NoSQL DB | Azure (Basic Experience) | DevOps (Docker)|| Agents | LLM / GenAI API FastAPI Python Deployment: Docker Kubernetes GitHub Actions Azure: Cosmos DB Redis (Cache) Azure AI Search GenAI: LLMs OpenAI GPT Claude Monitoring & Logging: Datadog

Nice to have

Azure AI Search Microsoft Fabric Kafka Snowflake Terraform Docker / Kubernetes Microsoft Purview