Staff Engineer

Key Responsibilities

·       ML Platform Architecture: Design and scale distributed ML platform for data, feature store, training, and inferencing; build multi-region, containerized infrastructure using Kubernetes and Terraform.

·       Training & Inferencing at Scale: Optimize GPU/TPU/CPU utilization for large-scale training and real-time inferencing; implement distributed training, model parallelism, and caching with Ray.

·       Performance & Governance: Drive FinOps-aligned architecture, auto-scaling, and efficiency; enable observability, SLA/SLO tracking, and incident management.

·       Collaboration & Leadership: Partner with cross-functional teams to deliver high-impact ML solutions; mentor engineers and set best practices for ML systems design.

Technology Stack

ML Frameworks: TensorFlow, PyTorch

Serving: Triton, TensorFlow Serving, TorchServe

Distributed Compute: Ray, Kubernetes, Spark, GPU/TPU optimization

Lifecycle: MLflow and AirFlow

Infra: AWS/GCP/Azure, Terraform, CI/CD

What We’re Looking For

·       9–14 years in ML platform/distributed systems.

·       Strong in Python, Kubernetes, GPU/TPU optimization.

·       Proven ability to design fault-tolerant, high-throughput ML systems.