Senior MLOps Engineer

Job Title:

Experience:

Location:

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About the Role

MLOps Engineer

Key Responsibilities

• Model Deployment & Automation:

• Design, build, and maintain CI/CD pipelines for ML model training, validation, and deployment.
  
• Automate the end-to-end ML lifecycle from data ingestion to model monitoring and retraining.
  

• Infrastructure & Scalability:

• Deploy and manage ML workloads on

  cloud platforms

  (AWS / Azure / GCP).
• Implement scalable infrastructure using

  Kubernetes, Docker

  , and

  Terraform

  .
• Optimize compute resources for training and inference workloads.
  

• Monitoring & Governance:

• Implement model monitoring (drift detection, performance tracking, and versioning).
  
• Manage model governance, reproducibility, and compliance requirements.
  
• Ensure observability across pipelines using tools like

  Prometheus

  ,

  Grafana

  , or

  ELK stack

  .

• Collaboration & Process Improvement:

• Work with data scientists to productionize and containerize models.
  
• Partner with software and DevOps teams to integrate ML systems into broader products.
  
• Continuously evaluate and improve MLOps frameworks, tools, and best practices.
  

Required Skills and Qualifications

• Technical Expertise:

• Strong programming skills in

  Python

  (preferred),

  Go

  , or

  Java

  .
• Experience with

  ML frameworks

  : TensorFlow, PyTorch, Scikit-learn.
• Proficient in

  MLOps tools

  such as MLflow, Kubeflow, Airflow, SageMaker, Vertex AI, or Azure ML.
• Strong knowledge of

  Docker

  ,

  Kubernetes

  , and

  CI/CD systems

  (Jenkins, GitHub Actions, Argo CD, etc.).
• Solid understanding of

  cloud infrastructure

  (AWS/GCP/Azure) and

  infrastructure-as-code

  tools (Terraform, CloudFormation).

• Data & Model Management:

• Familiarity with

  feature stores

  ,

  data versioning tools

  (DVC, Delta Lake, etc.), and

  model registries

  .
• Experience implementing

  monitoring and logging

  for ML models in production.

• Soft Skills:

• Strong problem-solving, debugging, and analytical skills.
  
• Excellent communication and cross-functional collaboration abilities.
  
• Ability to mentor junior engineers and establish best practices.
  

Preferred Qualifications

• Experience with

  large-scale distributed systems

  or

  real-time model serving

  .
• Exposure to

  MLOps security

  , compliance, and data governance frameworks.
• Knowledge of

  A/B testing

  ,

  online learning

  , or

  reinforcement learning

  systems.
• Contributions to open-source MLOps or DevOps projects.
  

Education

• Bachelor’s or Master’s degree in

  Computer Science, Data Engineering, Artificial Intelligence

  , or a related field.
• Certification in

  Cloud (AWS, GCP, or Azure)

  or

  MLOps

  is a plus.