Cloud and AI Leader: End-to-End Machine Learning Solutions Architect

2 days ago

Mumbai, Maharashtra, India beBeeMLOps Full time ₹ 15,00,000 - ₹ 25,00,000
Lead MLOps Engineer - Cloud & AI Expert

Job Description:

We are seeking an experienced Lead MLOps Engineer to lead and support the end-to-end deployment, monitoring, and optimization of machine learning and data-driven applications across cloud platforms. You will collaborate with data scientists, engineers, and business stakeholders to ensure scalable, secure, and highly available ML systems.

Key Responsibilities:

  • Design and develop complete MLOps solutions from scratch, leveraging expertise in cloud platforms, containerization, and orchestration.
  • Lead the development, deployment, and monitoring of ML pipelines 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.

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.

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