Machine Learning Operation Manager

7 days ago

India Weekday AI Full time ₹ 12,00,000 - ₹ 36,00,000 per year

This role is for one of the Weekday's clients

Min Experience: 6 years

Location: Remote (India)

JobType: full-time

As the Machine Learning Operations Manager, you will oversee the end-to-end ML lifecycle — from model training and deployment to monitoring and optimization. You will lead a small, high-performing team of engineers while remaining hands-on in building scalable, reliable, and efficient ML infrastructure. This role combines strategic leadership with deep technical expertise to ensure smooth collaboration between research, engineering, and operations teams.

Requirements

Key Responsibilities:

  • End-to-End ML Lifecycle: Manage training infrastructure, experiment tracking, deployment, and continuous optimization.
  • Collaboration with Researchers: Partner with research teams to streamline training, evaluation, and fine-tuning workflows.
  • Team Leadership: Mentor and guide a small team of ML engineers (3–4) while contributing as an individual contributor.
  • Performance Optimization: Improve latency, throughput, and cost efficiency; ensure robust packaging and runtime reliability.
  • Automation & Reliability: Develop systems for CI/CD, versioning, rollback, A/B testing, monitoring, and alerting.
  • Infrastructure Management: Maintain scalable, secure, and compliant AI environments across training and inference stages.
  • Cloud & AI Integration: Collaborate with cloud providers (AWS, GCP, Azure) and AI platforms to enhance tooling and optimize costs.
  • Cross-Functional Collaboration: Support GenAI and AI-driven projects across teams beyond core MLOps responsibilities.
  • Architecture & Roadmap: Contribute to architectural planning, documentation, and the continuous evolution of the ML stack.
  • Best Practices: Promote automation, MLOps standards, and operational excellence throughout the ML lifecycle.

Requirements:

  • 5+ years of hands-on experience in MLOps or ML/AI Engineering.
  • Strong understanding of ML/DL concepts and applied experience in model training and deployment infrastructure.
  • Proficiency with cloud-native ML tools (e.g., GCP Vertex AI, AWS SageMaker, Kubernetes).
  • Experience working across both model training and inference systems.
  • Familiarity with model optimization methods such as quantization, distillation, TensorRT, or FasterTransformer.
  • Demonstrated ability to lead complex technical projects independently.
  • Excellent communication and collaboration skills with a cross-functional mindset.
  • Ownership-oriented approach with comfort in driving clarity in ambiguous situations.

Skills:
MLOps, ML Engineering, Machine Learning Infrastructure, Model Deployment, Model Monitoring, CI/CD, Vertex AI, AWS SageMaker, GCP AI Platform, Kubernetes, Docker, MLflow, Kubeflow.

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