AI Automation Engineer

Job Description Roles & Responsibilities 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. Desired Candidate Profile 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. Employment Type Full Time Company Industry ShipbuildingShip RepairShip MaintenanceDrydocks Department / Functional Area Software DevelopmentApplication Development (IT Software) Keywords Software EngineeringAutomation Or System IntegrationCloud AIWorkflow AutomationRobotics Automation EngineerMachine LearningAI Automation Specialist Get real-time job updates only on our App