AI Platform Engineer

Cornelis Networks, Inc.•Austin, TX

1d•Remote

About The Position

Cornelis Networks is seeking an AI Platform Engineer to help shape how our engineering organization uses AI to design, develop, validate, and support advanced networking products. This is an emerging role at the intersection of software engineering, developer platforms, infrastructure, workflow automation, and applied AI. You will help build and expand a private, secure AI platform for our engineering organization, including a growing workforce of AI agents that automate meaningful engineering work. These capabilities will support engineers working across software development, Linux kernel drivers, firmware, embedded systems, ASIC development, hardware/software integration, validation, and high-performance networking. Cornelis Networks has already invested in a working AI platform and initial agent capabilities. You will help take that foundation further by designing, implementing, operating, and continuously improving the tools that enable our engineers to work more effectively. This is a rare opportunity to help define the future of engineering! The final solution does not yet exist. You will have the opportunity to experiment, learn from results, improve solutions based on feedback, and help establish new engineering practices. Candidates are not expected to have held this exact job title before. This discipline is still emerging, and we are more interested in your engineering fundamentals, creativity, implementation skills, curiosity, and potential to grow into broader platform and architectural responsibility. This is primarily a software and platform-engineering role. A background in machine-learning research or training large language models is not required. The focus is on applying existing AI capabilities reliably, securely, and cost-effectively to real engineering workflows.

Requirements

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. Familiarity with software development fundamentals, including version control, testing, debugging, and code review.
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. Ability to explain how MCP was used to connect an AI system to tools or external systems.
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. Experience should go beyond simple prompt experimentation, basic chatbots, or using an AI assistant to generate text.
Retrieval-Augmented Generation: Experience building or integrating a RAG workflow that grounds model output in documentation, code, databases, files, or other authoritative information. Familiarity with ingestion, chunking, embeddings, vector search, metadata, source context, or response evaluation is valuable.

Nice To Haves

Platform architecture and software-system design
Shell scripting and automation
CI/CD pipelines and automated validation
Docker and/or Podman
REST API development and integration
Workflow automation across developer or enterprise systems
FastAPI or a similar Python web framework
GitHub, Jira, Confluence, Microsoft Teams, or comparable APIs
PostgreSQL, Redis, or similar data infrastructure
Agent evaluation, observability, prompt engineering, or model selection
Embedded systems, firmware, semiconductors, ASICs, or hardware/software integration
Developer tools, internal platforms, or inner-source engineering
Microsoft Teams bot development or Power Automate
Experience operating production services or internal developer platforms

Responsibilities

Own the AI Platform: Own the configuration, tooling, and infrastructure that gives every Cornelis engineer a private, domain-aware AI assistant. Keep it current, reliable, and tuned to the specific technical domains our engineers work in - not generic web development tasks, but low-level systems work: drivers, firmware, ASIC register maps, hardware/software integration.
Build Out the Agent Workforce: Design, implement, and improve a growing workforce of autonomous agents that automate engineering operations. Each new agent you build becomes a permanent part of how the engineering organization operates. The backlog of planned agents is substantial and the opportunity to shape what gets built and how is real.
Design and Implement New Agents: Take agents from concept to production: FastAPI REST API, CLI interface, and chat integration. Work with engineering teams to identify the highest-value automation opportunities, define the agent's behavior, and build it to the platform's standards - deterministic where possible, LLM-powered where it adds real value, and cost-conscious throughout. The platform routes work across a tiered model fleet; knowing when to use a lightweight model versus a heavy one is part of the job.
Maintain and Improve the Infrastructure: Keep the platform running reliably: containerized services on Linux, reverse proxies, systemd timers, PostgreSQL and Redis, secrets management, and enterprise integrations with GitHub, Jira, Confluence, and Microsoft Teams. Debug infrastructure issues, manage deployments, and harden the platform as it grows.
Write and Improve Agent Skills and Prompt Engineering: Author and tune the structured workflows and system instructions that make AI agents useful for deep engineering work. Design agents that are reliable and grounded - not impressive in a demo but wrong in production.
Build CI/CD Validation Pipelines: Build and maintain automated validation that catches bad configurations, leaked credentials, and broken agent contracts before they land. Lead the inner-source contribution process - review PRs from engineers across teams, enforce standards, and make sure new additions are robust and cost-effective.
Manage Cost and Value Across the Platform: Track what the platform costs and what it delivers. Make deliberate decisions about model selection, token usage, and when AI is the right tool versus when deterministic code is cheaper and more reliable. Help engineers use AI tools effectively without wasting compute on low-value work. Report on platform value in terms engineering leadership can act on.
Track the AI Landscape and Keep the Platform Current: The tooling landscape is moving fast. Evaluate what matters, adopt what improves the platform, and upgrade before the team falls behind. Bring recommendations to engineering leadership with clear reasoning on capability and cost.