Senior ML Ops Engineer

Job Description:
Pay Range: $110.07hr - $113.07hr

• The Senior ML Ops Engineer is responsible for enabling production deployment of machine learning models and building scalable, reliable MLOps platform capabilities.
• This role partners closely with Data Scientists, data engineers, and product teams to translate experimentation workflows into robust production systems.
• The ideal candidate will design, implement, and maintain ML infrastructure, CI/CD pipelines, monitoring frameworks, and governance controls within AWS environments.

• Enable production deployment of machine learning models.
• Partner with Data Scientists to prepare development code for production deployment, including refactoring, packaging, standardization, and performance optimization.
• Build and maintain CI/CD pipelines for model training, validation, and deployment.
• Support batch and real-time inference workflows using scalable AWS-native services.
• Develop and maintain model APIs for integration into user-facing products.
• Design and implement a centralized model registry to track versions, metadata, lineage, and promotion stages.
• Build and maintain a feature store to support consistent feature computation for training and inference.
• Establish standardized ML pipelines for data ingestion, training, evaluation, deployment, and monitoring.
• Define infrastructure-as-code patterns to provision and manage ML environments reliably.
• Implement monitoring for model performance, data drift, and operational health.
• Establish alerting and rollback strategies for production model failures.
  
  Partner with security and platform teams to ensure compliance, access controls, and auditability.
• Collaborate with Data Scientists, data engineers, architects, and product teams to ensure feature availability, freshness, and quality.
• Support agile product teams by communicating API design and delivery for integration into user products.

• Experience in MLOps, ML Engineering, or backend software engineering with ML systems.
• Minimum of 4 years of relevant professional experience.
• Strong experience building and operating ML systems in AWS environments.
• Proficiency in Python and experience with production ML frameworks and tooling.
• Experience building APIs and backend services for model inference.
• Hands-on experience with CI/CD pipelines, infrastructure as code, and containerization technologies.

• Strong understanding of the machine learning lifecycle and productionization of ML workflows.
• Expertise in AWS services such as Sage Maker, ECS, Lambda, Step Functions, S3, and IAM.
• Experience with infrastructure as code tools such as Terraform or Cloud Formation.
• Containerization and deployment experience using modern Dev Ops practices.
• Strong analytical, troubleshooting, and problem-solving abilities.
• Excellent communication skills with the ability to work across technical and non-technical stakeholders.

• Bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
• Relevant certifications in AWS or cloud technologies preferred.