AI/ML Engineer
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Before you apply
LocationIndia, Bengaluru, 560064
Company size10,000+ people
PostedNov 3, 2026 · 1d ago
Verified live20h ago
At a glanceSummarised by Seekless from the posting.
Proficiency in programming languages like Python, R, and SQL for data manipulation, analysis, and visualization
Ability to design, develop, and implement scalable machine learning and deep learning models
Data analysis skills for inspecting, cleansing, transforming, and modeling data
Experience with machine learning pipelines for end-to-end model training and deployment
Knowledge of model deployment to production environments
Skills in big data technologies and CI/CD pipelines
Expertise in Natural Language Processing (NLP)
Understanding of data architecture design
Familiarity with data processing frameworks like Apache Hadoop and Apache Spark
Ability to create technical documentation
Knowledge of deep learning techniques
Statistical analysis skills
Data engineering skills for collecting, storing, and analyzing data at scale
Skills
Python
R
SQL
Apache Hadoop
Apache Spark
Team/Opportunity:
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.
What you will do:
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Design, develop, and implement robust, scalable, and optimized machine learning and deep learning models, with the ability to iterate with speed
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Write and integrate automated tests alongside models or code to ensure reproducibility, scalability, and alignment with established quality standards
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Implement best practices in security, pipeline automation, and error handling using programming and data manipulation tools
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Identify and implement the right data-driven approaches to solve ambiguous and open-ended business problems, leveraging data engineering capabilities
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Research and implement new models, technologies, and methodologies and integrate these into production systems, ensuring scalability and reliability
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Apply creative problem-solving techniques to design innovative tools, develop algorithms and optimized workflows
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Independently manage and optimize data solutions, perform A/B testing, evaluate performance and evaluate performance of systems
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Understand technical tools and frameworks used by the team, including programming languages, libraries, and platforms and actively support debugging or refining code in projects
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Contribute to the design and documentation of AI/ML solutions, clearly detailing methodologies, assumptions, and findings for future reference and cross-team collaboration
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Collaborate across teams to develop and implement high-quality, scalable AI/ML solutions that align with business goals, address user needs, and improve performance
Required skills:
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Programming: Writing code to manipulate, analyze, and visualize data, often using languages like Python, R, and SQL.
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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.
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Data Analysis: Inspecting, cleansing, transforming, and modeling data to discover useful information, draw conclusions, and support decision-making.
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Machine Learning Pipelines: Using automated workflows that manage the end-to-end process of training and deploying machine learning models.
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Model Deployment: Making a trained machine learning model available for use in production environments.
Specialized Skills:
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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
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Natural Language Processing (NLP): Focusing on the interaction between computers and humans through natural language.
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Data Architecture: Designing and structuring of data systems, ensuring that data is stored, managed, and utilized efficiently
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Data Processing Frameworks: Using tools and libraries to process large data sets efficiently, such as Apache Hadoop and Apache Spark.
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Technical Documentation: Creating and maintaining documentation that explains the functionality, use, and maintenance of software or systems.
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Deep Learning: Using a subset of machine learning involving neural networks with many layers, used to model complex patterns in data.
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Statistical Analysis: Collecting and analyzing data to identify patterns and trends, and to make informed decisions.
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Data Engineering: Designing and building systems for collecting, storing, and analyzing data at scale.
Legal
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.
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