Machine Learning Operations (MLOps) Engineer

Requirements

Key Responsibilities

ML CI/CD & Deployment

• Design, build, and maintain CI/CD pipelines for Machine Learning workflows, including:
  
• Model training
  
• Model validation
  
• Model packaging
  
• Model deployment
  
• Ensure ML pipelines operate efficiently across development, testing, and production environments.
  

Model Deployment & Serving

• Implement and manage model deployment patterns, including:
  
• Batch inference
  
• Real-time inference
  
• Streaming inference
  
• Develop and maintain model serving infrastructure for scalable and reliable ML inference.
  

Model Observability & Monitoring

• Establish comprehensive model observability frameworks to monitor:
  
• Data drift
  
• Model performance degradation
  
• Latency
  
• System failures
  
• Bias and quality signals
  

Feature Engineering Infrastructure

• Build and manage feature pipelines and feature stores.
  
• Ensure data lineage, reproducibility, and traceability across ML workflows.
  

Experiment Management & Model Governance

• Operationalize experiment tracking frameworks.
  
• Manage model registry and artifact management systems, including:
  
• Versioning of code
  
• Versioning of datasets
  
• Versioning of models
  

Model Testing & Validation

• Define and automate testing frameworks for ML systems, including:
  
• Unit testing
  
• Integration testing
  
• Implement validation gates and model promotion criteria before deployment to production.
  

Security & Compliance

• Collaborate with security and compliance teams to implement:
  
• Access controls
  
• Secrets management
  
• Audit logging
  
• Risk management controls
  

Performance Optimization

• Optimize infrastructure for training and inference workloads, including:
  
• Autoscaling
  
• Resource right-sizing
  
• GPU utilization
  
• Workload scheduling
  
• Ensure efficient compute utilization and cost optimization.
  

Operational Excellence

• Develop and maintain:
  
• Operational runbooks
  
• SLAs (Service Level Agreements)
  
• SLOs (Service Level Objectives)
  
• Incident response processes
  
• Operational monitoring dashboards
  

Architecture & Platform Standards

• Contribute to reference architectures for machine learning platforms.
  
• Develop engineering standards, reusable templates, and best practices for ML product teams.
  

Required Skills & Expertise

• Strong experience in Machine Learning Operations (MLOps) and ML platform engineering
  
• Expertise in CI/CD pipelines for ML workflows
  
• Experience managing ML model deployment patterns (batch, real-time, streaming)
  
• Knowledge of model observability and monitoring
  
• Hands-on experience with feature pipelines and feature stores
  
• Experience implementing experiment tracking, model registry, and artifact management
  
• Familiarity with model testing frameworks (unit and integration testing)
  
• Strong understanding of ML governance, security, and compliance practices
  
• Experience with autoscaling infrastructure, GPU utilization, and workload scheduling
  
• Ability to build operational dashboards and incident management processes
  
• Strong experience designing ML reference architectures and reusable engineering templates
  

Key Focus Areas

• ML CI/CD pipelines
  
• Model deployment and serving infrastructure
  
• Model monitoring and observability
  
• Feature store management
  
• Experiment tracking and artifact management
  
• Testing automation for ML systems
  
• Security, compliance, and governance
  
• Cost optimization and GPU utilization
  
• Operational reliability (SLA/SLO/Incident management)
  

Benefits