Senior ML Ops Engineer

Senior MLOps Engineer

Key Responsibilities

• Design, develop, and maintain

  end-to-end MLOps pipelines

  to automate the machine learning lifecycle from training to deployment and monitoring.
• Collaborate with data scientists, ML engineers, and platform teams to

  operationalize ML models

  across cloud and hybrid environments.
• Build and manage

  containerized environments

  for training and inference using Docker and Kubernetes.
• Implement

  CI/CD workflows

  (e.g., GitHub Actions, Jenkins) for deploying ML models.
• Ensure

  observability and monitoring

  of models in production (latency, drift, performance, errors).
• Support model deployment to a variety of targets including APIs, applications, dashboards, and edge devices.
  
• Implement

  model versioning

  , rollback strategies, governance, and traceability using tools like MLflow or Kubeflow.
• Drive best practices across teams and provide technical mentorship on MLOps topics.
  
• Continuously evaluate and integrate new tools and technologies to improve MLOps capabilities.
  

Required Skills & Qualifications

• 8+ years of experience in software, data, or ML engineering, with 6

  + years in MLOps

  .
• Strong programming experience in

  Python

  ,

  SQL

  , and

  Spark/PySpark

  .
• Deep expertise in

  MLOps tools

  such as MLflow, Kubeflow, Airflow, etc.
• Experience with

  cloud platforms

  , preferably

  GCP

  (Vertex AI, GKE, Cloud Run) or

  AWS

  .
• Hands-on with

  Databricks

  ,

  FastAPI

  ,

  Docker

  ,

  Kubernetes

  .
• Proficient with

  CI/CD

  ,

  Git

  , and

  Infrastructure as Code

  (Terraform, Ansible).
• Knowledge of

  monitoring frameworks

  like Prometheus and Grafana.
• Experience in GCP , AWS is also acceptable.
  
• Strong communication and stakeholder management skills.
  

Preferred Qualifications

• Experience with building scalable, self-service ML infrastructure.
  
• Familiarity with

  model governance, compliance

  , and

  security

  in production environments.
• Prior work in building reusable and modular MLOps solutions for cross-functional teams.