ML Cloud Infrastructure Engineer

Job description

Job responsibilities

• Built reliable and reproducible pipelines that move data from raw capture through curated, versioned training datasets.
• Enabled training and evaluation workloads to run at scale with clear quality and reliability signals before deployment.
• Delivered self-service ML infrastructure that reduces friction and accelerates Autonomy, Data, and Software teams.
• Improved the observability, reliability, security, and cost efficiency of HavocAI’s ML infrastructure.
• Established scalable foundations that allow ML development and deployment to grow alongside HavocAI’s autonomy capabilities.

Job requirements

• 3+ years of experience in software engineering, infrastructure engineering, data engineering, ML infrastructure, or a related field.
• Strong programming experience in Python, with experience in Go, C++, or another systems-oriented language preferred.
• Experience building and operating production services, APIs, data pipelines, developer platforms, or infrastructure.
• Hands-on experience with ML workflows such as dataset preparation, model training, evaluation, or deployment.
• Experience with cloud infrastructure, preferably AWS, and Infrastructure as Code.
• Hands-on experience with Kubernetes and containerized environments.
• Strong understanding of production engineering fundamentals, including reliability, observability, testing, automation, and maintainability.
• Ability to work effectively across engineering disciplines and solve ambiguous technical problems with a high degree of ownership.