About The Position
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.
Key Responsibilities
- 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.
Required Qualifications
- 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, and cloud-native ML tools.
- Experience with ML lifecycle platforms (e.g., MLflow, TFX, Airflow).
- Deep understanding of model versioning, reproducibility, and deployment strategies.
Preferred Skills
- 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 & Biases) and CI/CD best practices for ML.
- Understanding of ML system reliability, cost optimization, and compliance standards.
Contributions to open-source ML Ops tools or communities
Chevron ENGINE supports global operations, supporting business requirements across the world. Accordingly, the work hours for employees will be aligned to support business requirements. The standard work week will be Monday to Friday. Working hours are 8:00am to 5:00pm or 1.30pm to 10.30pm.
