AI/ML Engineer(GenAI)
Maersk
Bengaluru, Karnataka, IndiaSENIOR
Generative AIMachine Learning
Job Description
Develop Generative AI systems at Maersk.
Responsibilities
- 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.
- 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.
- 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.
- 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
- Object-Oriented Programming
- Design patterns
- Unit testing and validation
- Version control
- Maintainable and scalable system design
Qualifications
- Cloud environments
- CI/CD pipelines
- Containerization (Docker)
- Monitoring and performance tracking
- Exposure to AI observability and governance practices
- Domain experience in logistics, scheduling, or operational optimization environments
- 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 accommodationrequests@maersk.com.
- 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
- SPECIALIZED SKILLS
- 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.
Nice to have
- Experience with AI observability.
- Knowledge of AI ethics.
Benefits
- Diverse and inclusive workplace.