AI Automation Engineer

AI Agent & Workflow Development

• Design, develop, test, and deploy AI agents and automation workflows using modern LLM APIs and agent frameworks that execute complex, multi‑step tasks.
• Translate end‑to‑end business workflows into structured logic and prompt sequences for automated execution.
• Implement both code‑centric and tool‑assisted workflows using automation platforms and orchestration tools.
• Integrate AI components with internal systems and data sources via APIs, webhooks, event triggers, and direct database interaction.
• Apply security best practices for handling sensitive data, API keys, and access control, ensuring compliance with relevant policies.

Production Engineering

• Build backend services, API endpoints, and agent interfaces that are reliable, secure, and maintainable in production. Implement software engineering best practices: version control, automated testing, CI/CD, observability, logging, and error handling.
• Apply security best practices for handling sensitive data, API keys, and access control, ensuring compliance with relevant policies. Performance Optimization & Quality
• Monitor agent outputs and system behavior to maintain performance, cost efficiency, and response quality.
• Refine prompts, workflows, and integrated logic based on real‑world usage, feedback loops, and evaluation metrics.
• Identify, address, and mitigate hallucinations, errors, and other quality issues in model‑driven outputs.

Documentation & Knowledge Sharing

• Produce clear technical documentation including system architecture, API contracts, integration guides, workflow maps, and prompt libraries.
• Collaborate with cross‑functional teams to communicate technical design and enable adoption.