AI/ML Engineer(GenAI)

APM Terminals; Bengaluru; On-site; INR 2,500,000 - 5,000,000; Full time

Job summary

Maersk seeks an AI/ML Engineer (GenAI) to design and deploy production‑grade Generative AI solutions that enhance terminal operations, automate workflows, and generate actionable insights for global container terminals.

You will build LLM-powered systems, data pipelines, and AI agents, collaborating across teams to deliver reliable, scalable AI capabilities that improve yard flows and vessel operations.

Qualifications

• 5+ years of industry experience building and deploying production-grade AI/ML systems.
• PhD or MSc in Machine Learning, Computer Science, Applied Mathematics, Statistics, Engineering, or related field.
• Experience with Generative AI systems, LLMs, and RAG architectures.
• Exposure to logistics, scheduling, or operational optimization domains is a plus.

Responsibilities

• Design and deploy production-grade GenAI solutions for operational intelligence.
• Build end-to-end data pipelines for context retrieval and grounded response generation.
• Collaborate with stakeholders to align AI solutions with business goals and performance metrics.

Skills

Python

SQL

NLP

Deep Learning

Data Analysis

Data Engineering

CI/CD

Docker

LLMs

GenAI

RAG

Embedding Search

Unit Testing

Education

PhD or MSc in ML/CS/Applied Math

Tools

Docker

Apache Hadoop

Apache Spark

Git

Job description

AI/ML Engineer (GenAI) A.P. Moller – Maersk

Data AI/ML (Artificial Intelligence and Machine Learning) Engineering involves the use of algorithms and statistical models to enable systems to analyze data, learn patterns, and make data-driven predictions or decisions without explicit human programming. AI/ML applications leverage vast amounts of data to identify insights, automate processes, and solve complex problems across a wide range of fields, including healthcare, finance, e-commerce, and more. AI/ML processes transform raw data into actionable intelligence, enabling automation, predictive analytics, and intelligent solutions. Data AI/ML combines advanced statistical modeling, computational power, and data engineering to build intelligent systems that can learn, adapt, and automate decisions.

A.P. Moller – Maersk is the global leader in container shipping services. The business operates in 130 countries and employs 80,000 staff. An integrated container logistics company, Maersk aims to connect and simplify its customers’ supply chains. Today, we have more than 180 nationalities represented in our workforce across 131 Countries and this mean, we have elevated level of responsibility to continue to build inclusive workforce that is truly representative of our customers and their customers and our vendor partners too.

The team – who are we:

We are an ambitious team with a shared passion for harnessing data, Generative AI, data science (DS), machine learning (ML), advanced simulation, optimization, and engineering excellence to create meaningful impact for our customers and operations worldwide. We are a team, not a collection of individuals. We value our diverse backgrounds, perspectives, and strengths. We foster trust, constructive challenge, and thoughtful debate. We hold one another accountable and cultivate a strong feedback culture that supports both professional growth and personal development. We are now seeking a Generative AI Scientist who is excited about designing and building intelligent AI systems, including LLM-powered applications, agentic frameworks, and advanced ML solutions, that enhance operational intelligence, automate complex workflows, and generate actionable insights for container terminals globally, helping optimize yard flows, vessel operations, and decision-making to drive efficiency and measurable business value.

Key Responsibilities

• Generative AI Solution Development Design, implement, and deploy production-grade Generative AI solutions that enhance operational intelligence and automation across terminal workflows. Develop LLM-powered systems including retrieval-based reasoning, AI copilots, and task-oriented AI agents that support decision-making in complex operational environments. Design robust data pipelines that enable reliable context retrieval, semantic understanding, and grounded response generation.
• AI System Engineering & Reliability Build and maintain end-to-end GenAI solution lifecycles, from data preparation and experimentation through deployment, monitoring, and iteration. Implement structured evaluation frameworks to measure output quality, reliability, latency, cost efficiency, and hallucination risk. Enhance system robustness through prompt design, tool integration, validation layers, and guardrail mechanisms. Contribute to production readiness through testing, observability, and performance optimization practices.
• Collaboration & Business Alignment Work closely with stakeholders to translate operational challenges into structured AI problem statements with measurable success criteria. Communicate model behaviour, limitations, and trade-offs clearly to technical and non-technical audiences. Own delivery of solutions within defined architectural patterns and engineering standards.

To do this job, we imagine you have:

• 5+ years of industry experience building and deploying production-grade AI/ML systems, with strong hands‑on experience in Generative AI systems.
• PhD or M.Sc. in Machine Learning, Computer Science, Applied Mathematics, Statistics, Engineering, or related quantitative discipline (or equivalent practical experience).

Strong Generative AI Foundation

• Large Language Models (LLMs) and Transformer architectures
• Retrieval-Augmented Generation (RAG) systems
• Embedding‑based semantic search
• Prompt engineering and structured reasoning workflows
• AI agents or tool‑integrated LLM systems
• You understand how to evaluate model outputs for reliability, faithfulness, performance, and cost efficiency in real‑world environments.

Strong Software Engineering Foundation

• Object‑Oriented Programming Design patterns
• Unit testing and validation
• Version control
• Maintainable and scalable system design
• Deployment & Operational Mindset
• Cloud environments
• CI/CD pipelines
• Containerization (Docker)
• Monitoring and performance tracking

Nice to Have

• Experience with vector databases and semantic indexing platforms
• Exposure to AI observability and governance practices
• Domain experience in logistics, scheduling, or operational optimization environments
• Experience integrating GenAI systems with optimization or simulation models

Maersk is committed to a diverse and inclusive workplace, and we embrace different styles of thinking. Maersk is an equal opportunities employer and welcomes applicants without regard to race, colour, gender, sex, age, religion, creed, national origin, ancestry, citizenship, marital status, sexual orientation, physical or mental disability, medical condition, pregnancy or parental leave, veteran status, gender identity, genetic information, or any other characteristic protected by applicable law. We will consider qualified applicants with criminal histories in a manner consistent with all legal requirements. We are happy to support your need for any adjustments during the application and hiring process. If you need special assistance or an accommodation to use our website, apply for a position, or to perform a job, please contact us by emailing [e-mail].

CORE SKILLS

• Programming: Writing code to manipulate, analyze, and visualize data, often using languages like Python, R, and SQL. Proficiency Level: Proficient
• AI & Machine Learning: Creating systems that can perform tasks that typically require human intelligence. Using Machine learning (ML), a subset of AI that uses algorithms to learn from and make predictions based on data. Proficiency Level: Proficient
• Data Analysis: Inspecting, cleansing, transforming, and modeling data to discover useful information, draw conclusions, and support decision‑making. Proficiency Level: Foundational
• Machine Learning Pipelines: Using automated workflows that manage the end-to-end process of training and deploying machine learning models. Proficiency Level: Proficient
• Model Deployment: Making a trained machine learning model available for use in production environments. Proficiency Level: Proficient
• Big Data Technologies: Using continuous integration and continuous delivery (CI/CD) pipelines to automate the process of software development, including building, testing, and deploying code
• Natural Language Processing (NLP): Focusing on the interaction between computers and humans through natural language.
• Data Architecture: Designing and structuring of data systems, ensuring that data is stored, managed, and utilized efficiently
• Data Processing Frameworks: Using tools and libraries to process large data sets efficiently, such as Apache Hadoop and Apache Spark.
• Technical Documentation: Creating and maintaining documentation that explains the functionality, use, and maintenance of software or systems.
• Deep Learning: Using a subset of machine learning involving neural networks with many layers, used to model complex patterns in data.
• Statistical Analysis: Collecting and analyzing data to identify patterns and trends, and to make informed decisions.
• Data Engineering: Designing and building systems for collecting, storing, and analyzing data at scale.

Definition of Proficiency Levels: Foundational: This is the entry level of the skill, typically expected when starting a new role or working with the skill for the first time. You rely on strong manager support, coaching, and training as you build the capability to progress to higher proficiency levels. Proficient: This is the level at which you are considered effective in the skill. You demonstrate more than just functional competence—you begin to have a noticeable impact in your role by applying the skill consistently and meaningfully. You require only minimal support, coaching, or training to apply the skill successfully. Advanced: This is the level where you move beyond meeting expectations to actively leading, influencing, and delivering considerable impact across the wider business. You are seen as a role model, demonstrate the skill independently, and require little to no manager support.

A.P. Moller - Maersk is an integrated container logistics company working to connect and simplify its customer's supply chains. As the global leader in shipping services, the company operates in 130 countries and employs roughly 100,000 people. With simple end-to-end offering of products and digital services, seamless customer engagement and a superior end-to-end delivery network, Maersk enables its customers to trade and grow by transporting goods anywhere - all over the world. For more information click here. All the way.