Senior Artificial Intelligence and Machine Learning Platform Engineer

Maersk Line India Pvt Ltd; Bengaluru; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

Maersk in Bengaluru seeks a Senior AI/ML Engineer to design and scale AI Platform engineering initiatives. You will build end-to-end ML pipelines, model deployment, and governance to empower AI/ML teams, focusing on reliability, observability, and secure automation.

You will collaborate with data scientists and software engineers, mentor peers, and drive platform improvements, ensuring production readiness and clear documentation across cross-functional teams.

Qualifications

• Experience designing scalable AI/ML platforms and pipelines.
• Ability to deploy models into production with monitoring.
• Strong collaboration with data scientists and engineers.
• Experience with governance and observability for AI systems.

Responsibilities

• Independently design and implement scalable ML solutions and data systems.
• Collaborate with stakeholders to translate requirements into platform capabilities.
• Implement and refine feature engineering and ML pipelines.
• Deploy models in production with robust monitoring and reliability.
• Mentor team members through code reviews and knowledge sharing.
• Communicate decisions clearly to technical and non-technical stakeholders.

Skills

Kubernetes

ML Pipelines

Model Deployment

Data Engineering

Python

Tools

Apache Spark

Docker

CI/CD

Job description

Senior AI/ML Engineer - Platform A.P. Moller - Maersk A.P. Moller – Maersk is the global leader in container shipping services. The business operates in 130 countries and employs c. 80,000 staff. An integrated container logistics company, Maersk aims to connect and simplify its customers’ supply chains.

Job Role AI/ML Platform Engineering focuses on building scalable engineering platforms, infrastructure, automation, and operational capabilities that enable AI and Machine Learning teams to develop, deploy, integrate, monitor, and operate AI/ML solutions efficiently and securely. AI/ML platforms provide standardized tools and services for model lifecycle management, MLOps, model serving, Generative AI, LLM applications, agentic AI, experimentation, deployment, observability, and governance.

AI/ML Platform Engineering combines cloud infrastructure, software engineering, DevOps, MLOps, Kubernetes, automation, and AI technologies to create reliable and self-service platforms that accelerate enterprise AI adoption.

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.

• Independently design and implement scalable machine learning solutions and data systems, ensuring end to end workflows, large scale analytics and reliability
• Collaborate with stakeholders to translate business needs into data engineering solutions, evaluate user journeys and challenge business requirements to ensure seamless, value driven delivery and integration of solutions
• Implement and refine feature engineering, monitoring, ML pipelines, deploy models in production, and address challenges in data pipelines
• Apply innovative problem-solving techniques, leveraging advanced methodologies to find unique approaches to complex problems and improve outcomes
• Investigate and resolve complex challenges in data models and deployment to ensure reliable solutions that meet performance benchmarks
• Mentor team members through code reviews, pairing sessions, knowledge-sharing sessions, and contribute to Communities of Practice
• Communicate technology, infrastructure, and deployment decisions clearly to both technical and non-technical stakeholders while maintaining detailed documentation to ensure reproducibility, scalability, and understanding
• Ensure readiness for production releases, focusing on testing, monitoring, observability, and maintaining scalability and reusability of models for future projects
• Drive cross-team and cross-discipline initiatives to optimise workflows, remove redundant applications and processes, share best practices, and enhance collaboration between teams
• Demonstrate awareness of shared platform capabilities and actively identify opportunities to leverage them in designing efficient and scalable data engineering solutions

CORE SKILLS

Programming: Writing code to manipulate, analyse, and visualise 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 modelling data to discover useful information, draw conclusions, and support decision-making. Proficiency Level: Proficient

Machine Learning Pipelines: Using automated workflows that manage the end-to-end process of training and deploying machine learning models. Proficiency Level: Advanced

Model Deployment: Making a trained machine learning model available for use in production environments. Proficiency Level: Advanced

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 utilised 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 analysing data to identify patterns and trends, and to make informed decisions

Data Engineering: Designing and building systems for collecting, storing, and analysing 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.

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] .

Experience Level Senior Level