Machine Learning Operations Manager - Remote

Overview

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.

In Short

• Manage training infrastructure, experiment tracking, deployment, and continuous optimization.
• Partner with research teams to streamline training, evaluation, and fine-tuning workflows.
• Mentor and guide a small team of ML engineers (3–4) while contributing as an individual contributor.
• Improve latency, throughput, and cost efficiency; ensure robust packaging and runtime reliability.
• Develop systems for CI/CD, versioning, rollback, A/B testing, monitoring, and alerting.
• Maintain scalable, secure, and compliant AI environments across training and inference stages.
• Collaborate with cloud providers (AWS, GCP, Azure) and AI platforms to enhance tooling and optimize costs.
• Support GenAI and AI-driven projects across teams beyond core MLOps responsibilities.
• Contribute to architectural planning, documentation, and the continuous evolution of the ML stack.
• 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.

Benefits

• Opportunity to lead and shape a high-performing team.
• Work on cutting-edge ML infrastructure and technologies.
• Collaborative and innovative work environment.
• Flexible working hours and remote work.
• Professional development and growth opportunities.