Full-Time Machine Learning Operations Manager

• 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.

• 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.

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