Time since we first saw it
Day 40
Similar jobs usually come down between day 6 and day 70.
At Maersk, jobs usually come down by about day 22.
From 4,444 similar postings that came down, and 71 of Maersk’s. Coming down is not always a hire.
Business Data Insights Senior Specialist
India, Chennai, 600116
Live, first seen by us 40 days ago- Pay
- Pay not listed
- Work setting
- Not stated
- Employment
Full-time — employment source
Employment type in the employer’s posting: Full time
From the employer’s posting
What you’ll work on
Full postingThe role requires strong hands-on experience across both disciplines: developing ETL/ELT pipelines, transforming structured and semi-structured data, and ensuring reliable data availability, as well as building RAG pipelines, integrating LLM-based solutions, and operationalizing models with MLOps best practices.
The candidate should be comfortable working with business stakeholders, data scientists, BI teams, product teams, and technical teams to understand requirements, design scalable data and AI solutions, and deliver measurable business value end to end — from raw data to production-ready AI features.
Design and maintain ETL/ELT data pipelines on Azure Data Factory, Data Lake and Databricks
Build, fine-tune and deploy machine learning and generative AI models
Develop RAG pipelines and integrate LLM-based solutions with enterprise data
Monitor pipeline and model performance in production and troubleshoot failures
Implement data quality rules, validation and operational controls
From the employer’s posting
The role requires strong hands-on experience across both disciplines: developing ETL/ELT pipelines, transforming structured and semi-structured data, and ensuring reliable data availability, as well as building RAG pipelines, integrating LLM-based solutions, and operationalizing models with MLOps best practices.
The candidate should be comfortable working with business stakeholders, data scientists, BI teams, product teams, and technical teams to understand requirements, design scalable data and AI solutions, and deliver measurable business value end to end — from raw data to production-ready AI features.
Design, develop, test, deploy, and maintain scalable data pipelines using Azure Data Factory, Azure Data Lake, Azure Databricks, SQL, and related technologies.
Design, build, and fine-tune machine learning and generative AI models to solve business problems
Develop RAG pipelines and integrate LLM-based solutions with enterprise data sources and applications.
Monitor pipeline and model performance in production, troubleshoot failures, optimize processing/inference cost and latency
Implement data quality rules, responsible AI practices, exception handling, logging, audit checks, and operational controls.
Posting record
Report an error- Status in our records
- Live
- First seen by us
- Sep 26, 202640 days ago
- Recorded sightings
- 90
- Last seen by us
- Nov 5, 2026
- Employer says posted
- Sep 11, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
Maersk is hiring
14 open jobs
Open jobs by the week we first saw them, last 12 weeks.
- ML Engineer hiring is picking up
- Takes jobs down quickly
See how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- Python
- SQL
- Azure
- Databricks
- Docker
- Kubernetes
- Oracle
- Power BI
- Spark
- SQL Server
- SSRS
- PySpark
- PyTorch
- scikit-learn
- TensorFlow
Where the posting names them
- Experience with Azure Databricks, including notebooks, Spark SQL, PySpark, data transformation, and performance optimization. - Strong SQL and Python skills for querying, transformation, data validation, troubleshooting, and performance tuning. - Hands-on experience with Azure Machine Learning or Azure AI Studio for training, deploying, and managing ML models.
- Work with structured, semi-structured, and unstructured datasets, preparing them for both analytics and model training/evaluation. - Develop data and model transformation logic using SQL, PySpark, Spark SQL, Databricks notebooks, and Python ML frameworks. - Create and maintain reliable data flows and AI services across raw, curated, and consumption-ready layers, including production model inference.
Cloud & AI Platform: Azure Machine Learning, Azure AI Studio, Azure OpenAI Service Programming & Querying: SQL, Python, PySpark, Spark SQL AI/ML Frameworks: PyTorch, TensorFlow, scikit-learn, LangChain, Semantic Kernel
- Working knowledge of Azure Data Lake Storage for storing, organizing, and managing large volumes of data. - Experience with Azure Databricks, including notebooks, Spark SQL, PySpark, data transformation, and performance optimization. - Strong SQL and Python skills for querying, transformation, data validation, troubleshooting, and performance tuning.
Key Responsibilities - Design, develop, test, deploy, and maintain scalable data pipelines using Azure Data Factory, Azure Data Lake, Azure Databricks, SQL, and related technologies. - Build automated ETL/ELT pipelines to ingest, transform, validate, and publish data for analytics, reporting, and AI model consumption.
DevOps & Delivery: Git, Azure DevOps, CI/CD, Docker, Kubernetes, Agile delivery practices Legacy or Enterprise Systems: Informatica, SSIS, Oracle, Teradata, SQL Server 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 looking for a skilled and versatile Data & AI Engineer who can own the full data-to-AI lifecycle — designing and building robust data pipelines using Azure Data Lake, Azure Data Factory, and Azure Databricks, and then using that data to build, fine-tune, and deploy machine learning and generative AI solutions with Azure Machine Learning and Azure OpenAI. The role requires strong hands-on experience across both disciplines: developing ETL/ELT pipelines, transforming structured and semi-structured data, and ensuring reliable data availability, as well as building RAG pipelines, integrating LLM-based solutions, and operationalizing models with MLOps best practices. The candidate should be comfortable working with business stakeholders, data scientists, BI teams, product teams, and technical teams to understand requirements, design scalable data and AI solutions, and deliver measurable business value end to end — from raw data to production-ready AI features. Job Description
Role Summary We are looking for a skilled and versatile Data & AI Engineer who can own the full data-to-AI lifecycle — designing and building robust data pipelines using Azure Data Lake, Azure Data Factory, and Azure Databricks, and then using that data to build, fine-tune, and deploy machine learning and generative AI solutions with Azure Machine Learning and Azure OpenAI. The role requires strong hands-on experience across both disciplines: developing ETL/ELT pipelines, transforming structured and semi-structured data, and ensuring reliable data availability, as well as building RAG pipelines, integrating LLM-based solutions, and operationalizing models with MLOps best practices.
- 4+ years of experience spanning data engineering and AI/ML engineering, with strong exposure to data integration, ETL/ELT development, and cloud-based data and AI platforms. - Hands-on experience with Azure Data Factory for building, scheduling, monitoring, and managing data pipelines. - Working knowledge of Azure Data Lake Storage for storing, organizing, and managing large volumes of data.
- Hands-on experience with Azure Data Factory for building, scheduling, monitoring, and managing data pipelines. - Working knowledge of Azure Data Lake Storage for storing, organizing, and managing large volumes of data. - Experience with Azure Databricks, including notebooks, Spark SQL, PySpark, data transformation, and performance optimization.
- Strong SQL and Python skills for querying, transformation, data validation, troubleshooting, and performance tuning. - Hands-on experience with Azure Machine Learning or Azure AI Studio for training, deploying, and managing ML models. - Practical experience with Azure OpenAI Service or similar LLM platforms, including prompt engineering, fine-tuning, and model integration.
- Hands-on experience with Azure Machine Learning or Azure AI Studio for training, deploying, and managing ML models. - Practical experience with Azure OpenAI Service or similar LLM platforms, including prompt engineering, fine-tuning, and model integration. - Experience building RAG pipelines using vector databases (e.g., Azure AI Search, FAISS, Pinecone) for grounding LLM responses in enterprise data.
- Practical experience with Azure OpenAI Service or similar LLM platforms, including prompt engineering, fine-tuning, and model integration. - Experience building RAG pipelines using vector databases (e.g., Azure AI Search, FAISS, Pinecone) for grounding LLM responses in enterprise data. - Experience with ML/AI frameworks such as PyTorch, TensorFlow, scikit-learn, LangChain, or Semantic Kernel.
- Graduate or postgraduate degree in Computer Science, Information Technology, Data Engineering, Artificial Intelligence, Data Science, or a related field. - Relevant certifications in Azure Data Engineering, Azure Databricks, Azure Data Factory, Azure Machine Learning, or generative AI technologies will be an added advantage. Preferred Technology Stack
Preferred Technology Stack Cloud & Data Platform: Azure Data Lake, Azure Data Factory, Azure Databricks Cloud & AI Platform: Azure Machine Learning, Azure AI Studio, Azure OpenAI Service
Cloud & Data Platform: Azure Data Lake, Azure Data Factory, Azure Databricks Cloud & AI Platform: Azure Machine Learning, Azure AI Studio, Azure OpenAI Service Programming & Querying: SQL, Python, PySpark, Spark SQL
Data & AI Engineering: ETL, ELT, data pipelines, data modelling, data quality, RAG pipelines, prompt engineering, MLOps Vector Search & Data: Azure AI Search, FAISS, Pinecone, vector databases BI & Reporting: Power BI, SSRS or equivalent visualization tools
BI & Reporting: Power BI, SSRS or equivalent visualization tools DevOps & Delivery: Git, Azure DevOps, CI/CD, Docker, Kubernetes, Agile delivery practices Legacy or Enterprise Systems: Informatica, SSIS, Oracle, Teradata, SQL Server
- Experience with data modelling, data quality checks, data profiling, reconciliation, feature engineering, and model evaluation. - Exposure to DevOps practices such as Git, CI/CD, containerization (Docker, Kubernetes), version control, and deployment processes. - Working knowledge of Power BI or similar reporting tools, and knowledge of legacy ETL tools (Informatica, SSIS, Teradata, Oracle) will be an added advantage.
- Exposure to DevOps practices such as Git, CI/CD, containerization (Docker, Kubernetes), version control, and deployment processes. - Working knowledge of Power BI or similar reporting tools, and knowledge of legacy ETL tools (Informatica, SSIS, Teradata, Oracle) will be an added advantage. Key Responsibilities
Vector Search & Data: Azure AI Search, FAISS, Pinecone, vector databases BI & Reporting: Power BI, SSRS or equivalent visualization tools DevOps & Delivery: Git, Azure DevOps, CI/CD, Docker, Kubernetes, Agile delivery practices
- Experience building RAG pipelines using vector databases (e.g., Azure AI Search, FAISS, Pinecone) for grounding LLM responses in enterprise data. - Experience with ML/AI frameworks such as PyTorch, TensorFlow, scikit-learn, LangChain, or Semantic Kernel. - Good understanding of data lake architecture (bronze, silver, gold / medallion architecture) and MLOps practices (model versioning, CI/CD for ML, monitoring, retraining).
Programming & Querying: SQL, Python, PySpark, Spark SQL AI/ML Frameworks: PyTorch, TensorFlow, scikit-learn, LangChain, Semantic Kernel Data & AI Engineering: ETL, ELT, data pipelines, data modelling, data quality, RAG pipelines, prompt engineering, MLOps
Your next step
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Demand for ML Engineer roles
New ML Engineer postings at the employers we follow are up 12% over the last 5 weeks compared with the 5 before.
Each bar is one week of new postings. The darkest bars are the last 5 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
About the role
We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
US government figures for computer and information research scientists
- Median yearly pay, May 2025
- $140,300
- Expected change, 2025 to 2035
- 21.8% more jobsagainst 3.5% for all jobs
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
Job description
We are looking for a skilled and versatile Data & AI Engineer who can own the full data-to-AI lifecycle — designing and building robust data pipelines using Azure Data Lake, Azure Data Factory, and Azure Databricks, and then using that data to build, fine-tune, and deploy machine learning and generative AI solutions with Azure Machine Learning and Azure OpenAI.
The role requires strong hands-on experience across both disciplines: developing ETL/ELT pipelines, transforming structured and semi-structured data, and ensuring reliable data availability, as well as building RAG pipelines, integrating LLM-based solutions, and operationalizing models with MLOps best practices.
The candidate should be comfortable working with business stakeholders, data scientists, BI teams, product teams, and technical teams to understand requirements, design scalable data and AI solutions, and deliver measurable business value end to end — from raw data to production-ready AI features.
The role requires strong hands-on experience across both disciplines: developing ETL/ELT pipelines, transforming structured and semi-structured data, and ensuring reliable data availability, as well as building RAG pipelines, integrating LLM-based solutions, and operationalizing models with MLOps best practices.
The candidate should be comfortable working with business stakeholders, data scientists, BI teams, product teams, and technical teams to understand requirements, design scalable data and AI solutions, and deliver measurable business value end to end — from raw data to production-ready AI features.
Job Description
Role: Data & AI Engineer
Work Experience
5+ years of combined experience in data engineering and AI/ML engineering, including building scalable data pipelines and cloud data platforms as well as developing and deploying machine learning or generative AI solutions, in an Agile or DevOps environment.
Role Summary
We are looking for a skilled and versatile Data & AI Engineer who can own the full data-to-AI lifecycle — designing and building robust data pipelines using Azure Data Lake, Azure Data Factory, and Azure Databricks, and then using that data to build, fine-tune, and deploy machine learning and generative AI solutions with Azure Machine Learning and Azure OpenAI.
The role requires strong hands-on experience across both disciplines: developing ETL/ELT pipelines, transforming structured and semi-structured data, and ensuring reliable data availability, as well as building RAG pipelines, integrating LLM-based solutions, and operationalizing models with MLOps best practices.
The candidate should be comfortable working with business stakeholders, data scientists, BI teams, product teams, and technical teams to understand requirements, design scalable data and AI solutions, and deliver measurable business value end to end — from raw data to production-ready AI features.
Key Skills
- 4+ years of experience spanning data engineering and AI/ML engineering, with strong exposure to data integration, ETL/ELT development, and cloud-based data and AI platforms.
- Hands-on experience with Azure Data Factory for building, scheduling, monitoring, and managing data pipelines.
- Working knowledge of Azure Data Lake Storage for storing, organizing, and managing large volumes of data.
- Experience with Azure Databricks, including notebooks, Spark SQL, PySpark, data transformation, and performance optimization.
- Strong SQL and Python skills for querying, transformation, data validation, troubleshooting, and performance tuning.
- Hands-on experience with Azure Machine Learning or Azure AI Studio for training, deploying, and managing ML models.
- Practical experience with Azure OpenAI Service or similar LLM platforms, including prompt engineering, fine-tuning, and model integration.
- Experience building RAG pipelines using vector databases (e.g., Azure AI Search, FAISS, Pinecone) for grounding LLM responses in enterprise data.
- Experience with ML/AI frameworks such as PyTorch, TensorFlow, scikit-learn, LangChain, or Semantic Kernel.
- Good understanding of data lake architecture (bronze, silver, gold / medallion architecture) and MLOps practices (model versioning, CI/CD for ML, monitoring, retraining).
- Experience with data modelling, data quality checks, data profiling, reconciliation, feature engineering, and model evaluation.
- Exposure to DevOps practices such as Git, CI/CD, containerization (Docker, Kubernetes), version control, and deployment processes.
- Working knowledge of Power BI or similar reporting tools, and knowledge of legacy ETL tools (Informatica, SSIS, Teradata, Oracle) will be an added advantage.
Key Responsibilities
- Design, develop, test, deploy, and maintain scalable data pipelines using Azure Data Factory, Azure Data Lake, Azure Databricks, SQL, and related technologies.
- Build automated ETL/ELT pipelines to ingest, transform, validate, and publish data for analytics, reporting, and AI model consumption.
- Design, build, and fine-tune machine learning and generative AI models to solve business problems, using the curated data pipelines as the foundation.
- Develop RAG pipelines and integrate LLM-based solutions with enterprise data sources and applications.
- Work with structured, semi-structured, and unstructured datasets, preparing them for both analytics and model training/evaluation.
- Develop data and model transformation logic using SQL, PySpark, Spark SQL, Databricks notebooks, and Python ML frameworks.
- Create and maintain reliable data flows and AI services across raw, curated, and consumption-ready layers, including production model inference.
- Perform data exploration, validation, reconciliation, model evaluation, and performance testing to ensure accuracy and reliability end to end.
- Collaborate with business stakeholders, data scientists, BI developers, product owners, and architects to convert requirements into technical data and AI solutions.
- Support data migration, system integration, and AI feature rollout, from legacy platforms to cloud-based data and AI platforms.
- Monitor pipeline and model performance in production, troubleshoot failures, optimize processing/inference cost and latency, and ensure timely availability.
- Implement data quality rules, responsible AI practices, exception handling, logging, audit checks, and operational controls.
- Contribute to documentation of data flows, model architecture, source-to-target mappings, technical designs, and operational support procedures.
- Participate in E2E product lifecycle activities including design, development, testing, deployment, run support, retraining, refactoring, and decommissioning of outdated solutions.
- Continuously improve existing data and AI engineering processes through automation, standardization, and reusable components.
Business and Technical Skills
- Strong understanding of both data engineering and AI/ML concepts — data pipelines, data integration, data modelling, model lifecycle, and cloud-based platforms.
- Ability to understand business requirements and translate them into scalable technical designs spanning data and AI.
- Experience in working with data structures, storage systems, model architectures, data quality frameworks, and system integrations.
- Good understanding of enterprise data flows, upstream and downstream dependencies, and AI/reporting consumption patterns.
- Ability to quickly analyze existing data and AI solutions and recommend improvements, automation opportunities, or alternative approaches.
- Strong problem-solving skills with the ability to troubleshoot issues across pipelines, databases, models, and application layers.
- Experience working in Agile teams with E2E ownership of deliverables, from data ingestion through to deployed AI features.
- Ability to work with both technical and non-technical stakeholders.
- Good documentation skills, including technical design documents, model cards, process flows, data mapping, and support guides.
General Skills
- Strong team player with the ability to work independently when required.
- High ownership mindset with a focus on delivering reliable and scalable data and AI solutions.
- Open to learning new technologies and applying them to improve existing processes.
- Strong analytical thinking and attention to detail.
- Ability to understand end-to-end business processes and data/AI dependencies.
- Good communication skills with the ability to explain technical data and AI concepts in a simple and clear manner.
- Proactive approach toward automation, optimization, and continuous improvement.
Qualifications
- Graduate or postgraduate degree in Computer Science, Information Technology, Data Engineering, Artificial Intelligence, Data Science, or a related field.
- Relevant certifications in Azure Data Engineering, Azure Databricks, Azure Data Factory, Azure Machine Learning, or generative AI technologies will be an added advantage.
Preferred Technology Stack
Cloud & Data Platform: Azure Data Lake, Azure Data Factory, Azure Databricks
Cloud & AI Platform: Azure Machine Learning, Azure AI Studio, Azure OpenAI Service
Programming & Querying: SQL, Python, PySpark, Spark SQL
AI/ML Frameworks: PyTorch, TensorFlow, scikit-learn, LangChain, Semantic Kernel
Data & AI Engineering: ETL, ELT, data pipelines, data modelling, data quality, RAG pipelines, prompt engineering, MLOps
Vector Search & Data: Azure AI Search, FAISS, Pinecone, vector databases
BI & Reporting: Power BI, SSRS or equivalent visualization tools
DevOps & Delivery: Git, Azure DevOps, CI/CD, Docker, Kubernetes, Agile delivery practices
Legacy or Enterprise Systems: Informatica, SSIS, Oracle, Teradata, SQL Server
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 accommodationrequests@maersk.com.
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- Pay
No pay amount identified in the saved description.
- Location & working pattern
India, Chennai, 600116
Working pattern and location restrictions need checking in the full posting.
- Work authorization
Legacy or Enterprise Systems: Informatica, SSIS, Oracle, Teradata, SQL Server 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 accommodationrequests@maersk.com.