Applied AI Engineer: Multi-Agent Tools & Dashboards

OSI Engineering, Inc.; Cupertino (CA); On-site; USD 130,000 - 190,000; Full time

Job summary

OSI Engineering, Inc. in Cupertino, CA is seeking an Applied AI Engineer to build and improve internal AI agents and the web tools that display their results.

About half the role is agent development and half is full-stack work, collaborating with ML and wireless engineers to turn needs into working tools for internal use. This position is focused on internal engineering tools rather than customer-facing products.

Qualifications

• 3–5 years of hands-on experience applying ML models in production or internal tools.
• 6–12 months of hands-on agent development, including multi-agent systems and tool integration.
• Full-stack experience: backend/API development plus a modern front-end framework (React or Angular).
• Strong Python, including PyTorch, NumPy, Pandas and scikit-learn.
• Experience with AI coding tools such as Claude Code or Codex.
• Nice to Have: LangGraph or AutoGen experience.
• Knowledge of LTE/5G cellular domain.

Responsibilities

• Maintain and extend internal AI agents in a multi-agent environment; add context and new features.
• Build web portals and dashboards (React or Angular) that display agent outputs to engineering users.
• Develop and maintain the backend services and APIs behind those agents and portals.
• Apply existing ML models to engineering data using Python (PyTorch, NumPy, Pandas, scikit-learn).
• Turn requirements from ML and cellular engineers into technical designs and working features.
• Write and run unit tests so new features don't break existing functionality.

Skills

ML in production

Agent development

Backend API development

React or Angular

Python (PyTorch)

NumPy / Pandas / scikit-learn

Claude/Codex

LangGraph / AutoGen

LTE/5G knowledge

Tools

PyTorch

Job description

OSI Engineering, Inc. in Cupertino, CA is seeking an Applied AI Engineer to build and improve internal AI agents and the web tools that display their results.

About half the role is agent development and half is full-stack work, collaborating with ML and wireless engineers to turn needs into working tools for internal use. This position is focused on internal engineering tools rather than customer-facing products.