MLOps Engineer

Lead / Architect MLOps Engineer

Note:

Key Responsibilities

• Design, develop, and maintain

  end-to-end MLOps pipelines

  for training, deployment, monitoring, and retraining of ML models.
• Build and manage

  CI/CD pipelines

  for machine learning workflows.
• Automate

  model deployment, monitoring, performance tracking

  , and alerting in production.
• Collaborate closely with

  data scientists and ML engineers

  to ensure models are production-ready.
• Implement tools and best practices for

  model versioning, testing, validation, and reproducibility

  .
• Ensure adherence to

  data governance, security, and compliance standards

  .
• Optimize ML workflows for

  scalability, reliability, and cost efficiency

  .
• Troubleshoot and resolve issues related to

  model performance, infrastructure, and deployments

  .
• Stay up to date with the latest

  MLOps tools, frameworks, and industry best practices

  .
• Provide technical leadership and architectural guidance across MLOps initiatives.
  

Required Skills & Qualifications

Education

• Bachelors or Masters degree in

  Computer Science, Data Science, Engineering

  , or a related field.

Experience

• 8+ years

  of experience in

  MLOps, DevOps, or related roles

  .
• Proven experience designing and implementing

  production-grade ML platforms

  .

Technical Skills

• Strong programming skills in

  Python

  .
• Hands-on experience with

  AWS SageMaker

  and

  Shakudo

  platforms.
• Experience with ML frameworks such as

  TensorFlow, PyTorch, Scikit-learn

  .
• Strong knowledge of

  CI/CD tools

  (Jenkins, GitHub Actions, etc.).
• Experience with

  containerization and orchestration

  : Docker, Kubernetes.
• Hands-on experience with

  cloud platforms

  (AWS preferred; Azure/GCP is a plus).
• Familiarity with

  monitoring and observability tools

  such as

  Prometheus and Grafana

  .
• Experience implementing

  model versioning, experiment tracking, and validation frameworks

  .
• Understanding of

  ML governance, security, and compliance

  best practices.