Director of Machine Learning, Platform Engineering

C3 AI is seeking an experienced Director of Machine Learning to lead our Machine Learning (ML) Platform Engineering team. In this role, you will be responsible for designing, building, and scaling our ML infrastructure to power AI-driven (including Generative AI) applications across the organization. You will work closely with cross-functional teams to ensure that our ML platform is robust, scalable, and optimized for performance, while driving innovation and continuous improvement.

This leadership role requires a hands-on technical leader who can balance strategic vision with execution, drive adoption of ML best practices, and foster a high-performance culture within the team.

Responsibilities

• Lead and manage a team of ML platform engineers focused on designing, building, and maintaining our ML infrastructure and tools.
• Define and drive the strategic roadmap for LLM completions, fine-tuning, deployment, and monitoring, ensuring scalability and efficiency
• Define and drive the strategic direction and roadmap for machine learning infrastructure, model deployment, and MLOps in alignment with business objectives.
• Architect and design scalable, reliable, and efficient end-to-end ML workflows, including model training, deployment, monitoring, and retraining.
• Collaborate with data engineering, AI research, and software teams to ensure seamless integration of ML models into production environments.
• Establish best practices for model governance, versioning, reproducibility, and monitoring to improve model lifecycle management.
• Oversee performance monitoring, alerting, and troubleshooting mechanisms for real-time and batch ML workloads.
• Partner with talent acquisition to grow the team by hiring and mentoring world-class ML platform engineers.
• Work cross-functionally to prioritize initiatives, track progress, and ensure timely delivery of ML platform improvements.
• Stay up to date with emerging technologies and industry trends in Generative AI, MLOps, distributed computing, and AI infrastructure to drive continuous innovation.

Qualifications

• Bachelor's or Master's degree in Computer Science, Machine Learning, AI, or a related field (PhD preferred).
• 12 years of experience in ML infrastructure, distributed systems, data engineering, or AI/ML platforms in a software company.
• 6 years of leadership experience managing highly technical teams in ML engineering, AI infrastructure, or MLOps.
• Strong technical background in machine learning frameworks (TensorFlow, PyTorch, Scikit-Learn), distributed training, and cloud-based ML platforms.
• Deep expertise in MLOps pipelines, model serving (TorchServe, TensorFlow Serving, MLflow, KServe), and model monitoring.
• Hands-on experience with cloud AI/ML services (AWS Sagemaker, GCP Vertex AI, Azure ML) and Kubernetes-based ML orchestration.
• Proficiency in programming languages such as Python, Java, or C with experience in ML-specific libraries and tools.
• Experience with large-scale distributed training, data pipelines, and feature stores.
• Strong understanding of CI/CD for ML, automated model retraining, and A/B testing methodologies.
• Excellent leadership, communication, and stakeholder management skills, with experience influencing cross-functional teams