Senior Machine Learning Engineer

Total Number of Openings 1 The Chevron Engineering and Innovation Excellence Center (ENGINE) in Bengaluru India brings together the resources and expertise of the Chevron global network with talent in India to enhance agility and technological innovation to optimize solutions for the world’s current and future energy challenges.  As one of the leading energy providers worldwide, Chevron is involved in the production of crude oil and natural gas, manufacturing of transportation fuels, lubricants, petrochemicals, and additives, and the development of enabling technologies. Chevron's vision is to be the global energy company most admired for its people, partnerships, and performance. With a clear purpose to develop affordable, reliable, ever-cleaner energy that enables human progress, we believe human ingenuity has the power to solve any challenge and overcome any obstacle. Meeting the world’s growing energy needs requires the pursuit of innovations and advancements that deliver a better future for all.

We are actively searching for a talented and experienced Machine Learning (ML) Engineer to join our team. As a Machine Learning Engineer, you will play a crucial role in the development and implementation of cutting-edge artificial intelligence products. Your responsibilities will involve designing and constructing sophisticated machine learning models, as well as refining and updating existing systems. In order to thrive in this position, you must possess exceptional skills in statistics and programming, as well as a deep understanding of data science and software engineering principles. Your ultimate objective will be to create highly efficient self-learning applications that can adapt and evolve over time, pushing the boundaries of AI technology. Join us and be at the forefront of innovation in the field of machine learning.

Model Deployment & Automation Design and manage CI/CD pipelines for ML models using tools like MLflow, Kubeflow, or SageMaker. Automate model training, validation, and deployment workflows. Infrastructure & Scalability Architect and maintain scalable ML infrastructure on cloud platforms (AWS, Azure, GCP). Optimize resource usage and model performance in production environments. Support distributed training and real-time inference systems. Monitoring & Governance Implement monitoring systems for model drift, performance, and data integrity. Ensure compliance with data governance, privacy, and security standards. Establish observability and reliability practices for ML systems (SLOs, alerting). Collaboration & Leadership Work closely with data scientists, software engineers, and DevOps teams to integrate ML solutions. Mentor junior ML engineers and contribute to technical leadership across projects. Tooling & Frameworks Develop reusable components and libraries for ML Ops workflows. Evaluate and integrate new tools and technologies to improve ML lifecycle management.

Bachelor’s or Master’s degree in Computer Science, Data Engineering, or related field. 8-10 years of experience in software engineering, data science, or ML Ops. Strong proficiency in Python, Docker, Kubernetes, cloud-native ML tools and Computer Vision. Experience with ML lifecycle platforms (e.g., MLflow, TFX, Airflow). Deep understanding of model versioning, reproducibility, and deployment strategies.

Specialized in computer vision or other domain-specific ML applications. Proven experience in productionizing end-to-end ML workflows, including data ingestion, feature engineering, deployment, and monitoring. Familiarity with model monitoring and observability tools (e.g., Roboflow, DataRobot, Evidently AI, Arize AI). Expertise in feature stores (e.g., Feast, Tecton) and model registries. Experience with distributed training frameworks (e.g., Horovod, Ray) and real-time inference systems. Knowledge of experiment tracking tools (e.g., MLflow, Weights &