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