Infra Engineer (MLOps Engineer)

**APPLICABLE ONLY FOR IMMEDIATE JOINERS**

Job Title:

Location:

Job Overview:

Infrastructure Engineer with strong MLOps expertise

Key Responsibilities:

• Design and implement

  MLOps pipelines

  for training, testing, deployment, and monitoring of ML models
• Build and maintain scalable

  cloud infrastructure

  (AWS, GCP, or Azure) to support AI/ML workloads
• Automate CI/CD workflows for ML models using tools like

  GitHub Actions, Jenkins, or GitLab CI

• Manage containerized environments using

  Docker

  and

  Kubernetes

• Monitor, log, and troubleshoot production ML systems to ensure high availability
• Ensure version control for models, datasets, and experiments using tools like

  MLflow, DVC, or Weights & Biases

• Collaborate with data scientists and backend teams to streamline model deployment and scaling
  

Required Skills & Experience:

• 6-8 years of experience in

  DevOps, Infrastructure Engineering

  , or

  MLOps

  roles
• Strong hands-on experience with

  cloud platforms

  (AWS, GCP, or Azure)
• Proficient in

  containerization (Docker)

  and

  orchestration (Kubernetes)

• Solid understanding of

  CI/CD pipelines

  ,

  infrastructure as code (Terraform, Ansible)

• Experience with

  ML pipeline orchestration

  tools such as

  Kubeflow, Airflow, or SageMaker Pipelines

• Familiarity with ML lifecycle management and versioning tools (MLflow, DVC, etc.)
• Proficiency in scripting languages:

  Python, Bash, or Shell

• Strong debugging and system monitoring skills (Prometheus, Grafana, ELK, etc.)
  

Preferred Qualifications:

• Experience supporting large-scale

  machine learning platforms in production

• Understanding of security, networking, and compliance in ML systems
• Knowledge of model serving frameworks (TensorFlow Serving, TorchServe, Triton, etc.)
• Certification in AWS/GCP or Kubernetes is a plus