ML Ops Engineer (Boston, MA) at Foundation EGI

Boston, Massachusetts, United States -

Full Time

Start Date

Immediate

Expiry Date

12 Aug, 26

Salary

0.0

Posted On

14 May, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

ML Ops, Python, TypeScript, Google Cloud Platform, AWS, Docker, Kubernetes, Terraform, CI/CD, Vertex AI, LLMs, Prometheus, Grafana, ELK Stack, Computer Graphics, Physics-based Simulation

Industry

technology;Information and Internet

Description

Requirements: Architect, build, and operate end-to-end ML pipelines for training, validation and deployment on Google Cloud and AWS. Define, instrument, and maintain logging, monitoring, and alerting for model performance and data drift. Automate CI/CD for ML artifacts and infrastructure using GitHub Actions or equivalent. Collaborate with cross-functional teams, including frontend engineers, backend engineers, research engineers, and infrastructure engineers. Write clean, well-documented, fast, and maintainable code. Help ensure our systems have high availability and performance. Experience in computer graphics or physics-based simulation. Background in setting up Prometheus/Grafana, ELK, or similar monitoring stacks. Experience with Vertex AI. Experience working with custom Domain-Specific Languages. About Us: We are an MIT-born, venture-backed Silicon Valley startup building a real-life 'Jarvis'—an AI Copilot for design and manufacturing. Our goal is to utilize advanced AI, physics simulation, and computer graphics to reduce costs and improve engineering productivity across all steps of the design and manufacturing process. \n What we're looking for BS in Computer Science or a related field. 5+ years of experience as a AI/ML Ops, DevOps, Infrastructure Engineer or equivalent. Expert-level Python and TypeScripts skills. Experience with Docker, Kubernetes, Terraform, Google Cloud and AWS. Deep understanding of machine learning models, including LLMs. Experience designing and maintaining CI/CD pipelines to fine-tune or train ML models. Excellent written and verbal communication skills. Bonus Points Experience in computer graphics or physics-based simulation. Background in setting up Prometheus/Grafana, ELK, or similar monitoring stacks. Experience with Vertex AI. Experience working with custom Domain-Specific Languages. Our tech stack Google Cloud, AWS Python, TypeScript Protobuf, gRPC Next.JS, React.JS GitHub Actions Docker, Kubernetes, Spinnaker PostgreSQL \n

Responsibilities

Architect and operate end-to-end ML pipelines for training and deployment on GCP and AWS. Maintain system monitoring, alerting, and CI/CD automation for ML artifacts and infrastructure.