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

1. 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.

1. 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.

1. 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.

1. 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.

1. 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.

1. 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.

1. Production Deployment

• Deploy containerized services via CI/CD to Kubernetes behind an application gateway, with centralized

monitoring, secrets management and asynchronous messaging.

1. 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.

1. 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)
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