Architect, build, and scale the end-to-end ML Ops pipeline, including training, fine-tuning, evaluation, rollout, and monitoring.
Design reliable infrastructure for model deployment, versioning, reproducibility, and orchestration across cloud and on-prem GPU clusters.
Optimize compute usage across distributed systems (Kubernetes, autoscaling, caching, GPU allocation, checkpointing workflows).
Lead the implementation of observability for ML systems (monitor drift, performance, throughput, reliability, cost).
Build automated workflows for dataset curation, labeling, feature pipelines, evaluation, and CI/CD for ML models.
Collaborate with researchers to productionize models and accelerate training/inference pipelines.
Establish ML Ops best practices, internal standards, and cross-team tooling.
Mentor engineers and influence architectural direction across the entire AI platform.

Deep hands-on experience designing and operating production ML systems at scale (Staff/Principal-level expected).
Strong background in ML Ops, distributed systems, and cloud infrastructure (AWS, GCP, or Azure).
Proficiency with Python and familiarity with TypeScript or Go for platform integration.
Expertise in ML frameworks: PyTorch, Transformers, vLLM, Llama-factory, Megatron-LM, CUDA / GPU acceleration (practical understanding)
Strong experience with containerization and orchestration (Docker, Kubernetes, Helm, autoscaling).
Deep understanding of ML lifecycle workflows: training, fine-tuning, evaluation, inference, model registries.
Ability to lead technical strategy, collaborate cross-functionally, and operate in fast-paced environments

Principal ML Ops Engineer

Key skills