Senior ML Infra engineer

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Requirements:

• 8+ years of experience in relevant technical roles (Software Engineering, Data Engineering, ML Ops, or Infrastructure), with a strong foundation to build and scale ML infrastructure
• Strong coding skills in Python, CI/CD, and Infrastructure as Code (Terraform, Ansible)
• Deep expertise in ML Infrastructure: training orchestration (Dagster, Airflow), feature stores, live model monitoring, and distributed/multi-GPU training (TensorFlow, PyTorch)
• Extensive cloud & scalability experience: deploying ML models on AWS/GCP, optimizing real-time inference, handling large-scale data pipelines, and implementing cost-efficient FinOps strategies

Responsibilities:

• Build and optimize automated ML training pipelines (MLflow, Dagster)
• Improve scalability and performance (multi-GPU, caching, distributed architectures)
• Deploy and optimize real-time inference systems to ensure sub-20ms latency at scale
• Implement monitoring and observability for models (Prometheus, Grafana, Evidently AI)
• Optimize cloud costs and resource management (AWS Spot Instances, auto-scaling Kubernetes, FinOps)