ML/AI Ops

Company Description Kaggle is a global community where practitioners, researchers, and enthusiasts collaborate to advance the frontier of artificial intelligence. The platform hosts AI competitions, benchmarks, and agentic evaluation frameworks that enable community-led innovation and real-world experimentation. Kaggle offers opportunities to work with large-scale datasets, cutting-edge models, and diverse problem domains. Team members contribute to building tools and infrastructure that empower millions of users worldwide. Joining Kaggle means helping shape the future of open, community-driven AI development.

Role Description The ML/AI Ops role is a full-time, on-site position based in the Mumbai Metropolitan Region. This role focuses on ensuring reliable, scalable, and efficient deployment, monitoring, and maintenance of machine learning and AI systems that support Kaggle’s platform and community. Day-to-day responsibilities include managing end-to-end ML pipelines, automating model deployment workflows, and optimizing infrastructure for training, inference, and evaluation workloads. The role involves collaborating closely with data scientists, engineers, and product teams to translate experimental models into robust production services, while maintaining high availability, performance, and security standards. The ML/AI Ops professional will also help implement observability tools, troubleshoot production issues, and contribute to continuous improvement of operational processes and documentation.

Qualifications

• Strong analytical skills to diagnose issues in ML pipelines, interpret performance metrics, and optimize AI workloads.
• Effective communication skills to collaborate with cross-functional teams, document processes, and present technical findings clearly.
• Operations management skills to oversee ML/AI system reliability, capacity planning, and incident response in production environments.
• Project management skills to coordinate deployment initiatives, manage timelines, and drive continuous improvement of ML/AI operations.
• Relevant experience with ML/AI platforms, MLOps tools (e.g., CI/CD, model versioning, monitoring), and cloud infrastructure services.
• Proficiency in scripting or programming (e.g., Python, Bash) and familiarity with containerization and orchestration (e.g., Docker, Kubernetes).
• Bachelor’s degree in Computer Science, Data Science, Engineering, or a related field, or equivalent practical experience.
• Experience working with large-scale data and distributed systems; prior exposure to AI competitions or research environments is a plus.