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AI/ML Engineer (Data Engineering + AI Focus)

Maersk
Bengaluru, IND
Full-time
Mid
Onsite
Discovered Today

Full Job Posting

Key Responsibilities

  • Partner with business, product, and engineering teams to define scalable data and AI/ML solutions aligned with measurable business outcomes.
  • Design, build, and maintain robust data pipelines and data platforms using Python, Spark/PySpark, and cloud-native services.
  • Develop and deploy end-to-end AI/ML solutions, including data ingestion, feature engineering, model training, evaluation, and production deployment.
  • Architect and optimize large-scale data processing systems on cloud platforms (Azure/AWS), ensuring performance, reliability, and cost efficiency.
  • Implement and manage MLOps and DataOps practices, including CI/CD pipelines, model versioning, monitoring, and automated retraining.
  • Work with LLMs and Generative AI systems, integrating them into production workflows using frameworks such as LangChain, LlamaIndex, or similar.
  • Design and maintain data models (batch and streaming) that support analytics, reporting, and AI use cases.
  • Ensure data quality, governance, and observability across pipelines and ML systems.
  • Optimize infrastructure and workloads for cost, scalability, and performance, including efficient use of compute and storage.
  • Collaborate with engineering teams to deploy solutions using containerization and orchestration (Docker, Kubernetes).
  • Mentor junior engineers and contribute to engineering excellence through code reviews and best practices.
  • We are looking for:
  • 7+ years of experience across Data Engineering and AI/ML engineering in production environments.
  • Strong programming skills in Python and SQL, with hands-on expertise in Spark/PySpark for large-scale data processing.
  • Deep experience with cloud platforms (Azure or AWS), including services for data engineering, ML, and distributed systems (e.g., Databricks, Synapse, EMR, S3, ADF, etc.).
  • Hands-on experience in building and deploying scalable data pipelines and ML systems.
  • Strong understanding of data modeling (data lakes, lakehouse, warehouse, medallion architecture).
  • Experience with MLOps frameworks (e.g., MLflow) and production model lifecycle management.
  • Practical experience with LLMs / Generative AI applications, including RAG, document processing, or workflow automation.
  • Experience with containerization (Docker) and orchestration (Kubernetes).
  • Strong understanding of system design, scalability, and cost optimization in cloud environments.
  • Experience with data quality, observability, and monitoring frameworks.
  • Ability to translate business problems into scalable technical solutions with measurable impact.
  • Prior experience in logistics, supply chain, or operations is a plus.
  • Experience in simulation, optimization, or operations research is an added advantage.
  • 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 [email protected].
  • 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. 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.

Qualifications

  • Experience with MLOps frameworks (e.g., MLflow) and production model lifecycle management.
  • Experience with containerization (Docker) and orchestration (Kubernetes).
  • Experience with data quality, observability, and monitoring frameworks.

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