Senior ML Ops Engineer

Senior MLOps Engineer

In this role, you will work closely with software engineers, data scientists, and DevOps teams to build and scale the core MLOps infrastructure that powers AI initiatives across the organization. Your responsibilities will include managing CI/CD pipelines, enhancing observability, maintaining infrastructure, and ensuring seamless end-to-end model deployment and monitoring.

deep expertise in MLOps

Key Responsibilities

• Design, develop, and maintain

  end-to-end MLOps pipelines

  to automate the machine learning lifecycle from training to deployment and monitoring.

• This role is a dedicated MLOps position with a strong focus on operational responsibilities. The MLOps Engineer will be primarily responsible for operationalizing machine learning solutions, managing infrastructure monitoring, ensuring observability, and maintaining robust CI/CD pipelines.

• 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

  GCP

  (Vertex AI, GKE, Cloud Run) .
• 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 Mandatory

• Strong communication and stakeholder management skills.
  

Preferred Qualifications

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

  Bachelors degree in Engineering (B.Tech) or a related field

  is required for this role.
• MLOps position with a strong focus on operational responsibilities. The MLOps Engineer will be primarily responsible for operationalizing machine learning solutions, managing infrastructure monitoring, ensuring observability, and maintaining robust CI/CD pipelines
  
• Familiarity with

  model governance, compliance

  , and

  security

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