Senior ML Ops Engineer

Responsibilities:

• Develop and implement automated pipelines for deploying, monitoring, and updating machine learning models in production environments
• Design and maintain CI/CD pipelines for ML models, ensuring smooth transitions from development to production
• Monitor model performance in real time, detect degradation or anomalies, and automate retraining and versioning processes
• Manage and optimize infrastructure for ML workflows, including AWS, Docker, and Kubernetes
• Collaborate with data scientists to understand model requirements and integrate models into production systems
• Ensure effective model and data versioning to support reproducibility and traceability
• Implement secure deployment practices and ensure compliance with data privacy laws and regulatory standards
• Document workflows, deployment processes, and best practices for cross-team knowledge sharing

Primary Skills:

• Cloud Platforms: AWS (S3, DynamoDB, EC2, VPC, Lambda, CloudWatch)
• CI/CD Tools: GitHub Actions
• Containerization & Orchestration: Docker, Kubernetes
• Machine Learning Services: SageMaker
• Scripting & Automation: Python, Bash, Terraform
• Monitoring: Datadog, AWS CloudWatch
• DevOps: Pipeline creation, automated deployment

Qualifications:

• Bachelor’s degree in Computer Science or related field
• 8 years of overall experience in cloud infrastructure and DevOps
• Minimum of 5 years specifically designing, building, and maintaining MLOps pipelines
• Proficient in Python and experienced with ML lifecycle automation tools
• Strong understanding of DevOps best practices, source control, continuous integration, and automated deployments
• Familiar with security practices including IAM, KMS, secrets management, and network security
• Experience with Agile methodologies, particularly in SCRUM environments
• Experience or familiarity with Generative AI, LLM, Langchain, LLMOps, and related tooling is a plus

Nice to Have:

• TypeScript
• Databricks
• AWS Glue

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