AI Automation Engineer

About the Role We are seeking a highly skilled engineer to deliver production-grade AI agents and business automations — systems engineered for reliability, scale and sustained business value in live enterprise environments. You will build agents and workflows that take real actions across enterprise applications - querying data, interpreting documents and emails, executing multi-system workflows, and preparing business transactions - while supporting the company's AI and analytics roadmap throughout the AI lifecycle: preparing and integrating enterprise data, evaluating AI vendors, tools and solutions, ensuring AI governance compliance, and building internal awareness of emerging AI technologies. Key Responsibilities • AI Agent Engineering & System Architecture • Design, develop and deploy autonomous and multi-agent systems with planning, reasoning, tool use, memory and state management — via agentic frameworks or direct LLM API integrations. • Deliver agents across pro-code and low-code tiers - Azure AI Foundry, LangChain and LangGraph and Microsoft Copilot Studio — selecting the right tier per use case. • Define strict tool contracts and structured output schemas, integrating agents with enterprise applications via REST APIs and Model Context Protocol (MCP). • Orchestrate multi-user, role-based agentic workflows with review-before-commit human approval before any write-back or dispatch, built as durable, long-running processes with persistent state and resumability. • Business Process Automation & Workflow Development • Analyze, document and redesign repetitive processes across Finance, Procurement, Sales, Production, Engineering, HR and IT, selecting the right automation approach for each. • Design, build and deploy workflows in Power Automate — approvals, notifications, escalations, scheduled and event-driven processes triggered via APIs and webhooks. • Build Copilot Studio custom agents with actions, topics, knowledge sources and custom connectors to enterprise applications, with error handling and logging built in. • Retrieval Engineering (RAG) • Build and optimize Retrieval Augmented Generation pipelines: chunking, embedding selection, hybrid search and re-ranking over vector databases. Continuously measure and tune retrieval quality for relevance and precision. • Data Engineering & Integration • Clean, integrate and validate data from ERP, production and data-warehouse environments; build ETL pipelines, data mapping and quality checks feeding AI initiatives. • Document & Email Automation • Automate extraction and validation of data from business documents and incoming emails using OCR and AI document intelligence, routing low-confidence cases for human review. • Reliability, Evaluation & Observability • Engineer agents and workflows to mission-critical standards retries with backoff, fallbacks and circuit breakers, plus error handling, production monitoring and root-cause analysis. • Trace every tool call and reasoning step. Run metrics-driven test pipelines and dashboards tracking success rates, latency, cost and business benefits. • Production Deployment • Deploy containerized services via CI/CD to Kubernetes behind an application gateway, with centralized monitoring, secrets management and asynchronous messaging. • Security & Safety • Defend agents against prompt injection with permission boundaries, input validation and output filters; enforce least-privilege access and full audit trails per AI governance and standard compliance policies. • AI Project Coordination, Research & Enablement • Gather and document AI use-case requirements; support UAT execution and issue management through go live. • Track emerging AI technologies and propose POC and pilot projects. • Support employee training in responsible AI usage and prepare guidance and onboarding documentation. Required Qualifications & Skills • Bachelor's degree in Computer Science, Software Engineering, IT, AI/ML or related discipline. • 5+ years in software engineering, automation or system integration, including 2+ years building LLM-based agentic systems in production. • Hands-on experience with agentic frameworks (LangChain, LangGraph, Microsoft Agent Framework or comparable) and LLM APIs (Azure OpenAI or similar): tool use, structured outputs, streaming, prompt and context engineering. • Practical experience building RAG pipelines with vector databases (pgvector, FAISS, Azure AI Search or similar). • Hands-on experience with Power Automate and Copilot Studio, and integrating enterprise applications through REST APIs, JSON, webhooks and MCP. • Strong Python; frontend development with React (Vite or similar tooling) for agent-facing user interfaces; Solid SQL and ETL / data pipeline development. • Experience with Microsoft Azure services (AKS, Functions, Logic Apps, Service Bus, Key Vault, Azure OpenAI / AI Foundry) and containerized production deployment (Docker, Kubernetes, CI/CD) strongly preferred. • Applied ML fundamentals — forecasting, classification, optimization, recommender systems — including statistical validation and anomaly detection on transactional business data is a strong plus. • Understanding of business workflows, approvals, financial controls and exception handling; strong communication skills in English.