MLOps Engineer

MLOps Engineer

Job Summary

MLOps Engineer

Key Responsibilities

• Design and maintain

  end-to-end MLOps pipelines

  (training, validation, deployment, monitoring)
• Automate

  CI/CD workflows

  for machine learning models
• Deploy ML models using

  Docker and Kubernetes

• Implement

  model versioning, experiment tracking, and monitoring

• Monitor

  model performance, data drift, and system health

• Collaborate with Data Science and Engineering teams for production rollout
  
• Optimize inference performance, scalability, and reliability
  
• Ensure security, reproducibility, and compliance of ML systems
  

Required Skills

Technical Skills

• Strong experience in

  Python

• Hands-on experience with

  Docker, Kubernetes

• Experience with

  CI/CD tools

  (GitHub Actions, GitLab CI, Jenkins)
• Knowledge of

  ML deployment frameworks

  (FastAPI, Flask, TensorFlow Serving, TorchServe)
• Familiarity with

  Linux systems

MLOps & ML Tools

• Experience with

  MLflow, Kubeflow, Airflow

  or similar tools
• Knowledge of

  model lifecycle management

• Understanding of

  data pipelines and workflow orchestration

• Experience with

  monitoring tools

  (Prometheus, Grafana)

Cloud & Infrastructure (Any One Preferred)

• AWS / GCP / Azure
  
• Infrastructure as Code:

  Terraform / Helm

• Exposure to

  GPU-based ML workloads

  is a plus

Good to Have

• Experience with

  Computer Vision or NLP models

• Edge AI or real-time ML deployment experience
  
• Knowledge of

  Kafka / Streaming systems

• Experience in

  industrial or production ML systems

Education

• B.E / B.Tech / M.Tech / MCA in Computer Science, AI, Data Science, or related fields
  

Experience

• 3 to 8 years

  Role & responsibilities

Preferred candidate profile