AI Automation Engineer

We are looking for a hands-on AI Automation Engineer with strong experience in Python development, Generative AI, Agentic AI and enterprise automation.

The ideal candidate should be capable of independently building AI and automation solutions covering the complete engineering lifecycle, including development, integration, testing, deployment, troubleshooting and production support.

This is an Individual Contributor and hands-on engineering role, requiring strong coding and problem-solving capabilities.

Key Responsibilities

• Design, develop and maintain production-grade Python applications and automation solutions.
• Build Generative AI and Agentic AI applications using LLMs.
• Develop multi-step AI agent workflows using LangGraph, Semantic Kernel, AutoGen, CrewAI or similar frameworks.
• Build and optimize RAG pipelines, including embeddings, vector databases and semantic search.
• Integrate AI applications with enterprise systems through REST APIs.
• Develop backend services using FastAPI / Flask or similar Python frameworks.
• Build automation solutions using tools such as UiPath, Power Automate, Selenium, Playwright or similar technologies.
• Work with SQL and relational databases for data processing and automation.
• Implement appropriate guardrails, retries, validation and Human-in-the-Loop (HITL) mechanisms for AI workflows.
• Create unit, API, integration and end-to-end tests.
• Containerize applications using Docker.
• Work with Git and CI/CD pipelines for application deployment.
• Deploy AI/automation solutions into enterprise environments and troubleshoot production issues.
• Implement logging, monitoring and error-handling mechanisms to ensure production stability.

Required Skills

Must Have

• 46 years of overall IT/software engineering experience
• 3+ years of strong hands-on Python development
• Strong experience in Generative AI / LLM applications
• Hands-on exposure to Agentic AI / AI Agents
• Experience with LangGraph / Semantic Kernel / AutoGen / CrewAI or similar frameworks
• RAG, embeddings, vector databases and semantic search
• Azure OpenAI / OpenAI / Claude or other enterprise LLM platforms
• REST API development and integration
• FastAPI / Flask
• Strong SQL
• Git
• Docker
• CI/CD
• Automated testing
• Production deployment and troubleshooting

Good to Have

• UiPath / Power Automate
• Selenium / Playwright / PyAutoGUI
• React / JavaScript / TypeScript
• OCR / Document Intelligence
• Cloud platforms such as Azure or AWS
• AI observability, evaluation and guardrails
• MCP / tool calling
• Human-in-the-Loop workflows