Principle Data Scientist

We are looking for a strong hands-on engineer who enjoys building platforms, debugging complex distributed systems, and simplifying infrastructure for hundreds of ML and engineering users.

Responsibilities:

• Build the Platform Behind AI at Myntra: Myntra is looking for a Technical Lead - ML Platform and MLOps to help build the next generation of infrastructure that powers machine learning across Myntra. This is not a conventional MLOps operations role.
• You will work on some of the hardest engineering problems at the intersection of Machine Learning, Distributed Systems, Kubernetes, GPU infrastructure, Cloud, Developer Platforms and SRE.
• The platform you build will enable Data Scientists and ML Engineers to move from an idea to production faster: code, features, distributed training, experimentation, model registry, deployment, Inference, Observability.
• You will have the opportunity to design and build foundational ML platform capabilities used across multiple ML teams, influence platform architecture, solve large-scale infrastructure challenges, and establish engineering standards for how ML workloads are built and operated at Myntra.

Requirements:

• 6+ years of strong hands-on software/platform engineering experience.
• Strong programming skills in Python.
• Deep hands-on experience with Kubernetes, containers and Linux.
• Experience building or operating large-scale distributed systems or platform infrastructure.
• Strong understanding of cloud infrastructure including compute, storage and networking.
• Experience building production-grade CI/CD and automation platforms.
• Experience with workflow orchestration such as Apache Airflow or equivalent systems.
• Experience with ML lifecycle tooling such as MLflow or equivalent platforms.
• Strong understanding of ML training and deployment workflows.
• Experience with Infrastructure as Code, preferably Terraform.
• Strong debugging and production troubleshooting skills.
• Experience building systems with monitoring, logging, metrics and alerting.
• Strong fundamentals in system design, reliability and distributed computing.