Machine Learning Engineer

Position:

Experience:

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About the Role

Lead MLOps Engineer

Key Responsibilities:

• Be a hands-on contributor capable of independently designing and developing complete MLOps solutions from scratch.
• Lead end-to-end ML pipeline development, deployment, and monitoring across

  GCP

  and

  Azure

  .
• Build and maintain

  CI/CD pipelines

  using tools like ArgoCD, Git, and Docker.
• Automate and optimize ML model training, validation, deployment, and scaling using

  Kubernetes, Kubeflow, or similar orchestration platforms

  .
• Develop data processing workflows using

  Python and PySpark

  on distributed systems.
• Implement observability using tools like

  Grafana, NewRelic

  , and cloud-native monitoring solutions.
• Collaborate with Data Scientists to transition research into production-grade solutions.
• Guide and mentor junior engineers, enforce coding standards, and conduct code reviews.
• Demonstrate business understanding to align ML pipelines with product goals.
• Manage infrastructure as code (IaC) for reproducibility and scalability.
• Exposure to

  AI and RAG-related development

  , various

  GPU

  and

  AI Platforms

  required.

Required Skills & Experience:

• 9+ years of hands-on experience in MLOps roles.
• Strong proficiency in

  Python

  and

  PySpark

  with clean and scalable code practices.
• Expertise in

  GCP and Azure cloud platforms

  – including compute, storage, and networking components.
• Proven experience in deploying and managing containerized applications using

  Docker

  and

  Kubernetes

  .
• Hands-on with CI/CD tools – preferably

  ArgoCD, GitHub Actions, or GitLab CI

  .
• Experience in monitoring, logging, and alerting using tools such as

  Grafana, NewRelic, Prometheus

  , or similar.
• Understanding of ML model lifecycle, versioning, and performance monitoring.
• Experience with

  MLFlow

  for model versioning.
• Ability to create

  REST APIs

  using

  FastAPI, Flask, or Django

  .
• Strong problem-solving, communication, and stakeholder management skills.
• Experience mentoring teams and driving end-to-end project execution.
• Exposure to

  Vertex AI Pipeline in GCP

  or similar in other clouds is a plus.