Machine Learning Operations Specialist

2 weeks ago

Mumbai, Maharashtra, India beBeeMlOpser Full time ₹ 20,00,000 - ₹ 25,00,000

Job Title:

MLOps Engineer

Description:

As a Lead MLOps Engineer, you will play a crucial role in deploying, monitoring, and optimizing machine learning applications. Your primary responsibility will be to collaborate with cross-functional teams to ensure scalable, secure, and highly available ML solutions.

Key Responsibilities:

  • Develop complete MLOps solutions from scratch, incorporating innovative techniques and tools.
  • Lead the end-to-end ML pipeline development, deployment, and monitoring across cloud platforms, ensuring seamless integration and efficient execution.
  • Built and maintain CI/CD pipelines using tools like ArgoCD, Git, and Docker, ensuring continuous delivery and improvement.
  • Automate and optimize ML model training, validation, deployment, and scaling using Kubernetes, Kubeflow, or similar orchestration platforms.
  • Design and implement data processing workflows using Python and PySpark on distributed systems, guaranteeing high performance and scalability.
  • Implement observability using tools like Grafana, NewRelic, and cloud-native monitoring solutions, providing real-time insights and actionable data.
  • Collaborate closely with Data Scientists to transition research into production-grade solutions, leveraging their expertise to drive innovation.
  • Guide and mentor junior engineers, enforcing coding standards, conducting code reviews, and driving team growth.
  • Demonstrate a deep understanding of business goals, aligning ML pipelines to meet product objectives.
  • Manage infrastructure as code (IaC) for reproducibility and scalability, ensuring consistent and efficient operations.

Requirements:

  • At least 9 years of hands-on experience in MLOps roles, with a proven track record of success.
  • Strong proficiency in Python and PySpark, with a focus on 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 experience 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, with a focus on optimization and improvement.
  • Experience with MLFlow for model versioning, ensuring seamless tracking and management.
  • Ability to create REST APIs using FastAPI, Flask, or Django, with a focus on simplicity and efficiency.
  • Strong problem-solving, communication, and stakeholder management skills, with a proven ability to drive results and grow teams.
  • Experience mentoring teams and driving end-to-end project execution, with a focus on delivering high-quality outcomes.

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