ML & Cloud Infra Engineer — Scale AI Pipelines

OpenArc, LLC.; Belmont (CA); On-site; USD 120,000 - 150,000; Full time

Job summary

OpenArc, LLC. in the United States is seeking an experienced ML & Cloud Infrastructure Engineer to join our client’s organization. You will architect scalable cloud infrastructure for AI and data pipelines, and own deployment pipelines in a fast-paced startup environment.

You should have a track record delivering high-performance ML pipelines in production, with Python and familiarity with C++/Go, plus hands-on experience with AWS/GCP/Azure, Docker, Kubernetes and Airflow.

Qualifications

• Proven experience deploying high-performance ML pipelines in production.
• Strong Python skills; comfortable with C++/Go.
• Hands-on experience with cloud platforms (AWS, GCP, Azure).
• Familiarity with ML frameworks such as PyTorch.
• Experience with data-loading workflows and formats (Parquet, HDF5, TFRecord).
• Experience with containerization and orchestration tools (Docker, Kubernetes, Airflow).
• Ability to take ownership with light supervision; strong problem-solving skills.

Responsibilities

• Develop and deploy scalable ML training and validation pipelines in the cloud.
• Spin up distributed data ingestion, training and evaluation pipelines.
• Deploy monitoring and CI/CD pipelines.
• Enable large-scale evaluation of AI models via cloud-based metrics.
• Support simulations in the cloud for autonomy software evaluation.
• Optimize performance, I/O and GPU utilization.
• Build tooling and dashboards for experimentation, orchestration and visualization.
• Collaborate with multiple teams to integrate cloud tooling into workflows.

Skills

Python

C++/Go

PyTorch

Cloud platforms (AWS/GCP/Azure)

Data loading formats (Parquet/HDF5/TFR

Tools

Docker

Kubernetes

Airflow

Job description

OpenArc, LLC. in the United States is seeking an experienced ML & Cloud Infrastructure Engineer to join our client’s organization. You will architect scalable cloud infrastructure for AI and data pipelines, and own deployment pipelines in a fast-paced startup environment.

You should have a track record delivering high-performance ML pipelines in production, with Python and familiarity with C++/Go, plus hands-on experience with AWS/GCP/Azure, Docker, Kubernetes and Airflow.