Technical Lead (ML Platform & MLOps)

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.