Senior Machine Learning Operations Specialist

1 week ago

Pune, Maharashtra, India beBeeMLOps Full time ₹ 15,00,000 - ₹ 28,00,000
Senior Machine Learning Operations SpecialistWe are seeking an experienced Senior Machine Learning Operations Specialist to join our team. As a key member of our organization, you will be responsible for designing, developing, and maintaining end-to-end MLOps pipelines that automate the machine learning lifecycle from training to deployment and monitoring.Responsibilities
  • Pipeline Development: Design, develop, and maintain robust and scalable MLOps pipelines using industry-leading tools such as MLflow, Kubeflow, and Airflow.
  • Collaboration: Collaborate with data scientists, machine learning engineers, and platform teams to operationalize machine learning models across cloud and hybrid environments.
  • Containerization: Build and manage containerized environments for training and inference using Docker and Kubernetes.
  • CI/CD: Implement continuous integration and continuous delivery workflows using GitHub Actions, Jenkins, or similar tools.
  • Observability: Ensure observability and monitoring of models in production, including latency, drift, performance, and errors.
  • Model Deployment: Support model deployment to various targets, including APIs, applications, dashboards, and edge devices.
  • Model Versioning: Implement model versioning, rollback strategies, governance, and traceability using tools like MLflow or Kubeflow.
  • Mentorship: Drive best practices across teams and provide technical mentorship on MLOps topics.
  • Tool Evaluation: Continuously evaluate and integrate new tools and technologies to improve MLOps capabilities.
Requirements
  • Experience: 8+ years of experience in software, data, or machine learning engineering, with 6+ years in MLOps.
  • Programming Skills: Strong programming experience in Python, SQL, and Spark/PySpark.
  • MLOps Tools: Deep expertise in MLOps tools such as MLflow, Kubeflow, Airflow, etc.
  • Cloud Platforms: Experience with cloud platforms, preferably GCP (Vertex AI, GKE, Cloud Run) or AWS.
  • Databricks, FastAPI, Docker, Kubernetes, CI/CD, Git, and Infrastructure as Code (Terraform, Ansible).
  • Monitoring Frameworks: Knowledge of monitoring frameworks like Prometheus and Grafana.
Benefits
  • Scalable Infrastructure: Experience with building scalable, self-service ML infrastructure.
  • Model Governance: Familiarity with model governance, compliance, and security in production environments.
  • Reusable Solutions: Prior work in building reusable and modular MLOps solutions for cross-functional teams.
Note

Please ensure you have the required skills and experience before applying. We look forward to hearing from you

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