Aerones

Machine Learning Engineer

• Own the end-to-end ML pipeline for computer vision: data prep, training, evaluation, model packaging, artifact/version management, deployment, and monitoring (local GPU cluster + GCP).
• Design and maintain containerized workflows for multi-GPU training and distributed workloads (e.g., PyTorch DDP, Ray, or similar).
• Build and operate orchestration (e.g., Airflow/Argo/Kubeflow/Ray Jobs) for scheduled and on-demand pipelines across on-prem and cloud.
• Implement and tune resource allocation strategies based on current and upcoming task queues (GPU/CPU/memory-aware scheduling; preemption/priority; autoscaling).
• Introduce and integrate monitoring/telemetry for:
• - job health and failure analysis (retry, backoff, alerts),
• - data/feature drift and model performance (precision/recall, latency, throughput),
• - infra metrics (GPU utilization, memory, I/O, cost).
• Harden GCP environments (permissions, networks, registries, storage) and optimize for reliability, performance, and cost (spot/managed instance groups, autoscaling).
• Establish model governance: experiment tracking, model registry, promotion gates, rollbacks, and audit trails.
• Standardize CI/CD for ML (data/feature pipelines, model builds, tests, and canary/blue-green rollouts).
• Collaborate with CV researchers/engineers to productionize new models and improve training throughput & inference SLAs.
• Continuously improve documentation: update existing pipeline docs and produce concise runbooks, diagrams, and “how-to” guides.

MLOps Engineer