Remote AI Platform Engineer for Autonomous Agents

Cornelis Networks, Inc.

Austin (TX)

On-site

USD 140,000 - 180,000

Full time

14 days+

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Benefits offered by this job

Health insurance

Dental and vision coverage

Disability and life insurance

401(k) with company match

Open Time Off (OTO)

Job summary

Cornelis Networks, Inc. is seeking an AI Platform Engineer to help design, build, and scale an in-house AI platform for engineering teams. You will oversee private AI assistants, autonomous agents, and AI-enabled workflows that support drivers, firmware, ASICs, and software development.

This remote role focuses on practical, cost-aware AI integration in real engineering tasks. You will collaborate across Linux, AI tooling, and software platforms, shaping how engineers leverage AI for

Qualifications

B.S. or M.S. in Computer Science, Engineering, or a related discipline, or equivalent practical experience.
Python or Equivalent Programming Language: Experience building software, applications, scripts, or services in Python or a comparable general-purpose programming language, with the ability and willingness to work in Python.
Linux: Practical experience developing, deploying, operating, or troubleshooting software in a Linux environment.
Model Context Protocol: Hands-on experience implementing, integrating, extending, or operating MCP clients, servers, tools, or MCP-based workflows.
AI Agent Development: Hands-on experience building an AI agent or LLM-powered workflow that performs meaningful work using tools, APIs, structured workflows, files, databases, or external systems.
Retrieval-Augmented Generation: Experience building or integrating a RAG workflow grounding model output in documentation, code, databases, or files.

Responsibilities

Own the AI Platform: Own configuration, tooling, and infrastructure that gives every Cornelis engineer a private, domain-aware AI assistant.
Build Out the Agent Workforce: Design, implement, and improve a growing workforce of autonomous agents for engineering operations.
Design and Implement New Agents: Take agents from concept to production with REST API, CLI, and chat integration.
Maintain and Improve the Infrastructure: Keep the platform running with containerized services, proxies, and enterprise integrations.
Write and Improve Agent Skills and Prompt Engineering: Tune workflows and system instructions for reliable AI agents.
Build CI/CD Validation Pipelines: Validate configurations and credentials; review PRs; enforce standards and cost-effectiveness.
Manage Cost and Value Across the Platform: Track costs and value; decide on model usage and deterministic vs AI tooling.
Track the AI Landscape and Keep the Platform Current: Evaluate tooling; upgrade before the team falls behind; present clear reasoning to leadership.