Senior AI Productivity Engineer

Cornelis Networks, Inc.; United States; Hybrid; USD 150,000 - 210,000; Full time

Job summary

Cornelis Networks is seeking a Senior AI Productivity Engineer to shape how the engineering team uses AI for design, development, validation, and support of advanced networking products. You will build a private, secure AI platform and a growing workforce of AI agents to automate engineering work.

The role spans software and platform engineering with emphasis on reliability, cost-efficiency, and secure automation.

Responsibilities

• Own the configuration, tooling, and infrastructure that gives every Cornelis engineer a private, domain-aware AI assistant; keep it current and reliable.
• Design, implement, and improve a growing workforce of autonomous agents that automate engineering operations.
• Take agents from concept to production: FastAPI REST API, CLI interface, and chat integration for high-value automation.
• Maintain the platform with containerized services on Linux, reverse proxies, systemd timers, PostgreSQL and Redis, secrets management.
• Write and tune structured workflows and system instructions to keep AI agents reliable and production-ready.
• Build and maintain automated validation to catch bad configurations, leaked credentials, and broken agent contracts before they land.
• Manage platform cost and value by choosing models, token usage, and cost-effective implementations.

Job description

Cornelis Networks delivers high-performance scale-out networking solutions for AI and HPC datacentres. Our differentiated architecture integrates hardware, software, and system-level technologies to maximize the efficiency of GPU, CPU, and accelerator-based compute clusters at scale. Our solutions help customers push the boundaries of AI and HPC by eliminating bottlenecks and enabling massive-scale training, inference, simulation, and data-intensive workloads.

We are a fast-growing team of architects, engineers, and business professionals with a proven track record of building successful products and companies. As a global organization, our team spans multiple U.S. states and six countries, and we continue to expand with exceptional talent in onsite, hybrid, and remote roles.

Cornelis Networks is seeking a Senior AI Productivity 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
• 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.

What you will do:

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 w