ML Platform Engineer — Scalable AI Infra

agi-inc; Santa Fe (NM); On-site; USD 110,000 - 170,000; Full time

Benefits offered by this job

Medical insurance

Dental insurance

Vision insurance

Relocation assistance

Immigration support

Job summary

AGI, Inc. is seeking a software engineer to build end‑to‑end ML infrastructure and tooling for trustworthy consumer‑grade agents. You will design CI/CD pipelines for ML, create evaluation harnesses, and develop SDKs, CLIs, and lightweight UIs for researchers.

You will also implement observability dashboards and performance metrics, ensuring scalable, reliable experimentation and fast feedback for model iteration.

Qualifications

• Bachelor’s degree in Computer Science, Engineering, or equivalent practical experience.
• 3+ years in Software Engineering, MLOps, or ML Infrastructure.
• Strong Python proficiency.
• Experience building internal developer tools, CLIs, or dashboards.
• Experience with cloud infrastructure (AWS or GCP) and containerization (Docker, Kubernetes).

Responsibilities

• Training Automation: Design and implement robust CI/CD pipelines for machine learning workflows.
• Evaluation Infrastructure: Build scalable evaluation harnesses to benchmark models on every merge.
• Research Tooling: Develop internal SDKs, CLIs, and lightweight UIs to empower researchers.
• Observability & Performance: Implement tracking for latency, throughput, errors, GPU usage, and costs.

Skills

Software Engineering

Python

MLOps

Internal tools

Cloud proficiency

Education

Bachelor’s degree in CS/Engineering or equivalent

Tools

Docker

Kubernetes

AWS

GCP

Job description

AGI, Inc. is seeking a software engineer to build end‑to‑end ML infrastructure and tooling for trustworthy consumer‑grade agents. You will design CI/CD pipelines for ML, create evaluation harnesses, and develop SDKs, CLIs, and lightweight UIs for researchers.

You will also implement observability dashboards and performance metrics, ensuring scalable, reliable experimentation and fast feedback for model iteration.