Senior AI/ML Engineer - Platform

APM Terminals; Bengaluru; On-site; INR 2,500,000 - 4,500,000; Full time

Job summary

Maersk seeks an AI/ML Platform Engineer to design scalable ML platforms and data systems, enabling teams to develop, deploy, monitor, and operate AI/ML solutions securely. You will work across cloud, Kubernetes, and data pipelines to accelerate enterprise AI adoption.

You will collaborate with stakeholders to translate needs into robust data engineering solutions, implement feature engineering, and ensure production readiness with strong documentation and observability practices.

Qualifications

• Proficiency in programming languages such as Python, R, SQL.
• Experience building scalable ML platforms and data systems.
• Ability to deploy models in production and monitor performance.

Responsibilities

• 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 to improve outcomes and ensure production readiness
• Mentor team members through code reviews and knowledge-sharing sessions
• Communicate deployment decisions clearly to technical and non-technical stakeholders with thorough documentation
• Ensure readiness for production releases, focusing on testing and observability
• Drive cross-team initiatives to optimize workflows and reuse platform capabilities

Skills

Python

R

SQL

ML Pipelines

Model Deployment

Big Data

NLP

Data Architecture

Data Processing Frameworks

Technical Documentation

Deep Learning

Statistical Analysis

Data Engineering

CI/CD

Hadoop

Spark

Tools

CI/CD pipelines

Apache Hadoop

Apache Spark

Job description

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 optimize 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, 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: 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 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.

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].

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.