Machine Learning Engineer

Role: Machine Learning Engineer

Duration: 12 months

Location: Sunnyvale, CA

Direct client

Required Skills:

Overview

We are seeking a skilled Machine Learning Engineer with strong MLOps capabilities to help design, build, and scale machine learning systems from development through production. This role focuses on operationalizing models, building reliable infrastructure, and supporting data scientists in deploying and maintaining machine learning solutions.

Key Responsibilities

• Build, maintain, and scale MLOps pipelines, including model training, versioning, validation, deployment, and monitoring.
• Partner with data scientists to productionize machine learning models and ensure seamless deployment across environments.
• Develop tools, frameworks, and platforms that improve visibility into model performance, behavior, and lifecycle.
• Create and manage automated CI/CD workflows for ML assets, ensuring repeatable and reliable model releases.
• Implement observability best practices, such as logging, alerting, performance tracking, and drift detection.
• Optimize infrastructure to support high-performance model execution and scalable experimentation.
• Collaborate closely with engineering teams to integrate ML models into production systems.
• Maintain documentation, technical standards, and best practices for ML engineering and deployment processes.

Qualifications

Required

• Strong proficiency in end-to-end machine learning engineering, including data preparation, feature pipelines, deployment, and monitoring.
• Hands-on experience with MLOps tools such as MLflow, Kubeflow, SageMaker, Vertex AI, or similar.
• Backend or full-stack development experience with one or more languages (Python, Java, JavaScript/Node, Go, etc.).
• Familiarity with cloud environments (AWS, Google Cloud Platform, Azure) and containerization (Docker, Kubernetes).
• Experience building automated CI/CD pipelines for ML workflows.
• Strong understanding of model versioning, reproducibility, and experiment tracking.
• Ability to work in a fast-paced environment and collaborate across data science, engineering, and product teams.

Preferred

• Experience building internal ML platforms or developer tools.
• Knowledge of distributed systems and large-scale data processing (Spark, Flink, Beam, etc.).
• Familiarity with monitoring tools for ML models (e.g., Evidently AI, Fiddler, Arize, WhyLabs).
• Experience deploying multiple models in production environments.