ML Cloud Infrastructure Engineer

About the role

HavocAI is seeking a Machine Learning Cloud Infrastructure Engineer to design, build, and operate the platforms that power its autonomous systems. You will create end‑to‑end pipelines that move raw multi‑modal data into reproducible training sets, support large‑scale model training, and enable reliable model deployment in cloud environments.

Key responsibilities

• Develop and maintain data pipelines that ingest telemetry, imagery, video, sensor, and simulation data and produce versioned training datasets.
• Design reproducible training and evaluation workflows that scale across AWS compute and GPU resources.
• Build and operate model deployment infrastructure, including packaging, serving, inference, versioning, and rollback mechanisms.
• Implement experiment tracking, dataset lineage, and model versioning to ensure reproducible ML development.
• Design, provision, and manage scalable AWS infrastructure using Infrastructure as Code.
• Maintain Kubernetes/EKS clusters and containerized environments for training, batch processing, evaluation, and serving.
• Create self‑service tooling for compute scheduling, storage, data access, and deployment.
• Optimize utilization, scalability, and cost efficiency of cloud and accelerator resources.

Required profile

• Strong background in software, infrastructure, or data engineering with hands‑on experience in machine‑learning workflows.
• Comfortable working in fast‑paced, ambiguous environments and delivering production‑grade systems.
• Ability to design scalable, observable, secure, and reliable cloud solutions.

Required skills

• AWS cloud services
• Infrastructure as Code (e.g., Terraform, CloudFormation)
• Kubernetes and Amazon EKS
• Containerization technologies