Responsibilities
- Design and manage pipelines for ML model deployment and automate model training, validation, and deployment workflows.
- Architect and maintain scalable ML infrastructure on AWS, Azure, and GCP, including support for distributed training and real-time inference.
- Optimize resource usage and model performance in production environments.
- Implement monitoring for model drift, performance, and data integrity while establishing observability and reliability practices.
- Ensure compliance with data governance, privacy, and security standards.
- Collaborate with data scientists, software engineers, and DevOps teams to integrate ML solutions.
- Mentor junior ML engineers and contribute to technical leadership across projects.
- Develop reusable MLOps components and libraries and evaluate new tools for ML lifecycle management.
Requirements
- Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related field.
- 8-10 years of experience in software engineering, data science, or MLOps.
- Strong proficiency in Python, Docker, Kubernetes, cloud-native ML tools, and computer vision.
- Experience with ML lifecycle platforms such as MLflow, TFX, or Airflow.
- Deep understanding of model versioning, reproducibility, and deployment strategies.
- Experience productionizing end-to-end ML workflows covering data ingestion, feature engineering, deployment, and monitoring.
- Preferred: specialization in computer vision or another domain-specific ML application.
- Preferred: familiarity with Roboflow, DataRobot, Evidently AI, or Arize AI for monitoring and observability.
- Preferred: expertise with Feast or Tecton feature stores and model registries.
- Preferred: experience with Horovod or Ray, real-time inference systems, MLflow or Weights & Biases, and ML system reliability, cost optimization, and compliance standards.
Benefits
- Opportunity to participate in world-leading energy projects and advance professional development within an inclusive, collaborative workplace.
- Structured learning, mentorship, and opportunities to contribute to impactful projects.
- Comprehensive benefits supporting health, well-being, and work-life balance.
- Thoughtfully designed work environments with digital collaboration tools.
- Exposure to emerging technologies and digital solutions shaping the future of energy and enterprise.
- Standard Monday-to-Friday work week with scheduled hours of either 8:00am-5:00pm or 1:30pm-10:30pm.
- Chevron is unable to sponsor employment visas or consider individuals on time-limited visa status for this position.
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About Chevron
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