Reads like a ML Engineer role: putting models into production.
Bangalore, KA, IN, 560066
Open 6 months, first seen by us 192 days agoLead end-to-end delivery of AI/ML solutions, from scoping to monitoring
Develop GenAI/NLP applications and time-series, computer vision or commercial analytics models
Build production-ready solutions using MLOps practices
Apply statistical and machine learning methods to produce actionable insights and recommendations
Lead end-to-end delivery of AI/ML solutions: scoping, modeling, evaluation, deployment, and monitoring.
Develop GenAI/NLP applications, and/or time-series, computer vision, commercial analytics models.
Build production-ready solutions applying MLOps best practices (MLflow, CI/CD, monitoring, data quality).
Apply data science methods, machine learning tools, visualization and/or statistical techniques along with domain knowledge to generate actionable insights and provide optimized recommendations.
Prior experience with oil & gas, commercial domain, supply chain, production systems, wells or subsurface domain is highly desirable.
Experience working with Azure Databricks or other data science frameworks.
Experience with mathematical modeling, physics-based simulators, scientific computing and numerical methods would be an added advantage.
- Expertise in one or more of the following: Time Series Analysis, Computer Vision, Natural Language Processing, Generative AI, Commercial Analytics. - Master’s or Ph.D. degree from a recognized university in one of the following disciplines: Data Science, Computer Science, IT, Chemical Engineering, Mechanical, Civil, Materials, Aerospace, Geoscience/Geophysics, Applied Math or related disciplines with a minimum GPA of 7.0. - 5+ years of relevant experience in developing, delivering, and validating production-ready AI/ML solutions.
At ExxonMobil, our vision is to lead in energy innovations that advance modern living and a net-zero future.
In the employer’s words · Read in context
Open 6 monthsWe first saw this posting 192 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
Day 192
Up longer than most. Similar jobs usually come down between day 6 and day 70.
Well past ExxonMobil’s usual: its jobs come down by about day 53.
From 4,444 similar postings that came down, and 65 of ExxonMobil’s. Coming down is not always a hire.
22 open jobs
Open jobs by the week we first saw them, last 12 weeks.
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.
- In-depth knowledge and practical experience in statistical analysis techniques (e.g., classification, regression, time-series, Bayesian techniques) and machine learning techniques (e.g., decision trees, ensemble methods, deep learning, neural networks, causal analysis). - Practical experience in the full machine learning lifecycle from problem formulation, data acquisition, data cleaning to model building and deployment at enterprise level.Proficiency in Python or R, ML frameworks (PyTorch, TensorFlow, scikit-learn) and libraries (NumPy, pandas). - Experience with software engineering practices, agile methodologies and version control (Git).
- End‑to‑End ML Project Lifecycle - Python/R Programming Skills - Software Engineering & Agile Framework
- Prior experience with oil & gas, commercial domain, supply chain, production systems, wells or subsurface domain is highly desirable. - Experience working with Azure Databricks or other data science frameworks. - Experience with mathematical modeling, physics-based simulators, scientific computing and numerical methods would be an added advantage.
- Develop GenAI/NLP applications, and/or time-series, computer vision, commercial analytics models. - Build production-ready solutions applying MLOps best practices (MLflow, CI/CD, monitoring, data quality). - Apply data science methods, machine learning tools, visualization and/or statistical techniques along with domain knowledge to generate actionable insights and provide optimized recommendations.
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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.
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.
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.
About us
At ExxonMobil, our vision is to lead in energy innovations that advance modern living and a net-zero future. As one of the world’s largest publicly traded energy and chemical companies, we are powered by a unique and diverse workforce fueled by the pride in what we do and what we stand for.
The success of our Upstream, Product Solutions and Low Carbon Solutions businesses is the result of the talent, curiosity and drive of our people. They bring solutions every day to optimize our strategy in energy, chemicals, lubricants and lower-emissions technologies.
We invite you to bring your ideas to ExxonMobil to help create sustainable solutions that improve quality of life and meet society’s evolving needs. Learn more about our What and our Why and how we can work together.
Key Skills
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Exxon Mobil Corporation has numerous affiliates, many with names that include ExxonMobil, Exxon, Esso and Mobil. For convenience and simplicity, those terms and terms like corporation, company, our, we and its are sometimes used as abbreviated references to specific affiliates or affiliate groups. Abbreviated references describing global or regional operational organizations and global or regional business lines are also sometimes used for convenience and simplicity. Similarly, ExxonMobil has business relationships with thousands of customers, suppliers, governments, and others. For convenience and simplicity, words like venture, joint venture, partnership, co-venturer, and partner are used to indicate business relationships involving common activities and interests, and those words may not indicate precise legal relationships.
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Bangalore, KA, IN, 560066
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