AI/ML Engineer

Experteer Overview In this role, you will design and build production-grade Generative AI systems that improve terminal operations and decision-making. You will work within Global Data & Analytics to scale LLM-powered solutions, RAG, and AI agents across container terminals, driving operational intelligence and automation. You'll shape the end-to-end GenAI lifecycle, from data prep to deployment and monitoring, delivering measurable business value. This is a chance to contribute to a fast-moving, collaborative team tackling complex logistical challenges with AI at scale. Compensation / Benefits * Design, implement, and deploy production-grade GenAI solutions for terminal workflows * Develop LLM-powered systems including retrieval-based reasoning, AI copilots, and task-oriented agents * Build robust data pipelines for context retrieval, semantic understanding, and grounded responses * Manage end-to-end GenAI lifecycles from data prep to deployment, monitoring, and iteration * Implement evaluation frameworks to measure quality, reliability, latency, cost, and hallucination risk * Enhance robustness via prompt design, tool integration, and guardrails; ensure production readiness through testing and observability * Collaborate with stakeholders to translate operational challenges into AI problem statements with measurable success criteria * Communicate model behavior, limitations, and trade-offs to both technical and non-technical audiences * Own delivery of solutions within defined architectural patterns and standards Tasks * 5+ years of industry experience building and deploying production-grade AI/ML systems * PhD or M.Sc. in related quantitative discipline (or equivalent practical experience) * Strong experience with Large Language Models and Transformer architectures * Experience with Retrieval-Augmented Generation (RAG) and embedding-based semantic search * Proficiency in Python and solid software engineering fundamentals (OOP, design patterns, testing, version control) * Experience with cloud environments, CI/CD, Docker, and monitoring/observability * Ability to work in fast-paced, agile environments Key requirements *

AI evaluation frameworks to measure quality, reliability, latency, cost, and hallucination risk * Enhance robustness via prompt design, tool integration, and guardrails; ensure production readiness through testing and observability * Collaborate with stakeholders to translate operational challenges into AI problem statements with measurable success criteria * Communicate model behavior, limitations, and trade-offs to both technical and non-technical audiences * Own delivery of solutions within defined architectural patterns and standards Tasks * 5+ years of industry experience building and deploying production-grade AI/ML systems * PhD or M.Sc. in related quantitative discipline (or equivalent practical experience) * Strong experience with Large Language Models and Transformer architectures * Experience with Retrieval-Augmented Generation (RAG) and embedding-based semantic search * Proficiency in Python and solid software engineering fundamentals (OOP, design patterns, testing, version aaaa with * Experience with cloud environments, CI/CD, Docker, and monitoring/observability * Ability to work in fast-paced, agile environments Key requirements *