About the role
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.
Strong Differentiators:
- We would especially love to meet you if you have worked on Ray or other distributed computing frameworks.
- Distributed or multi-node ML training.
- GPU and multi-GPU infrastructure.
- Kubernetes-based ML platforms.
- ML training platforms used by multiple teams.
- Model serving and inference infrastructure.
- GPU scheduling and utilization optimization.
- Large-scale workflow orchestration.
- ML platform developer experience.
- Infrastructure cost and performance optimization.
- Feature platforms or feature stores.
Good to Have:
- Databricks, SageMaker, Vertex AI, or similar ML platforms.
- Model monitoring, data drift, and automated retraining.
- NVIDIA Triton, Ray Serve, vLLM, or SGLang.
- LLM training/inference and LLMOps.
- Vector databases and retrieval infrastructure.
- Model governance and lineage.
- OpenTelemetry, Grafana, or similar observability ecosystems.