AI Automation Engineer

AI Automation Engineer

Experience4–6 years (min. 3 years strong Python development)EducationBachelors in CS, IT, Engineering, or Data Science  

Role Overview

The AI Automation Engineer supports the Delivery Transformation Office in identifying, designing, developing, deploying, and supporting AI-led automation solutions across delivery programs. This is a fully hands-on engineering role — the successful candidate personally owns the complete journey from use-case discovery through architecture, development, testing, deployment, and production stabilization.

This is not a coordination, governance, or proof-of-concept-only role. You will write code, build integrations, create AI agents, troubleshoot failures, deploy applications, and keep solutions stable in production.   •     Build proof-of-concepts and convert successful pilots into production-ready, measurable solutions.

Hands-On AI & Automation Engineering

•     Design, develop, test, debug, and maintain Python-based automation solutions end to end.

•     Build modular, reusable components: automation scripts, REST APIs, background workers, scheduled jobs, data-processing utilities, reconciliation frameworks, AI-powered applications, and internal portals.

•     Apply strong engineering practices: modular design, type hints, exception handling, retry logic, structured logging, configuration management, secret handling, and unit testing.

•     Develop backend services using FastAPI or Flask; build lightweight UIs with React, JavaScript, or TypeScript where required.

Generative AI & Agent Development

•     Build GenAI solutions on Anthropic Claude, Claude Code, Azure OpenAI, OpenAI APIs, AWS Bedrock, and Google Vertex AI.

•     Write and optimize system prompts, structured outputs, prompt chains, tool instructions, and validation rules.

•     Build AI agents that retrieve knowledge, analyse data, call APIs, execute tools, generate reports, update systems, and escalate exceptions.

•     Develop agentic workflows using LangGraph, Semantic Kernel, AutoGen, CrewAI, or custom Python orchestration.

•     Implement human-in-the-loop controls, retries, checkpoints, fallback logic, execution traces, and guardrails.

•     Build RAG solutions from enterprise documents, databases, SharePoint, and knowledge repositories; evaluate outputs for accuracy, groundedness, and hallucination.  

Skills & Experience

Required

•     Strong hands-on Python development and automation engineering — able to independently build and deploy production-grade applications.

•     Practical experience with Generative AI, LLM APIs, prompt engineering, structured outputs, and tool calling; hands-on with Claude, Claude Code, or equivalent tools.

•     Experience building AI agents or multi-step agentic workflows.

•     Strong REST API, JSON, authentication, and enterprise integration skills.

•     Solid SQL and data-processing capability; experience with browser or desktop automation.

•     Strong knowledge of Git, Docker, CI/CD, automated testing, and production troubleshooting.

•     Experience deploying solutions into cloud or enterprise environments.

Preferred

•     LangGraph, Semantic Kernel, AutoGen, Claude Code, or similar agent frameworks.

•     Vector databases, embeddings, semantic search, and RAG pipeline design.

•     React, JavaScript, or TypeScript; Redis, Celery, ARQ, or event-driven processing.

•     OCR, document intelligence, multimodal AI; Kubernetes or serverless deployment.

•     AI evaluation, agent tracing, and LLM observability tooling.