AI Platform Engineer

Job Overview

Cornelis Networks develops high-performance scale-out networking solutions for AI and high-performance computing datacenters. Its technology combines hardware, software, and system-level capabilities to support large GPU, CPU, and accelerator-based compute clusters.

As an AI Platform Engineer, you will help shape how the company’s engineering teams use AI to design, develop, validate, and support advanced networking products.

Cornelis Networks already has a working AI platform and initial agent capabilities. Rather than starting from scratch, you will help expand that foundation by designing, implementing, operating, and continuously improving the platform and its growing workforce of AI agents.

This is primarily a software and platform engineering position. Machine-learning research or experience 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’ll Do

Own and Improve the AI Platform

Help own the configuration, tooling, and infrastructure that provides Cornelis engineers with private, domain-aware AI assistance. You’ll work to keep the platform reliable, current, secure, and relevant to specialized engineering domains such as drivers, firmware, ASIC register maps, and hardware/software integration.

Build an AI Agent Workforce

Design, implement, and improve autonomous agents that automate meaningful engineering operations. You’ll work with engineering teams to identify high-value opportunities and turn useful agent concepts into production capabilities.

Develop Production AI Agents

Take agents from initial concept through implementation and production. This can include building FastAPI REST APIs, CLI interfaces, chat integrations, structured workflows, and connections to external systems.

You will determine where deterministic software is the better solution and where an LLM provides meaningful additional value, while considering reliability and operating cost.

Maintain Platform Infrastructure

Help operate and improve containerized Linux services and supporting infrastructure, including reverse proxies, systemd timers, PostgreSQL, Redis, secrets management, deployments, and enterprise integrations.

The platform integrates with systems such as GitHub, Jira, Confluence, and Microsoft Teams, requiring strong software integration and troubleshooting skills.

Develop Agent Skills and Prompts

Write and refine structured workflows and system instructions that enable AI agents to perform deep engineering tasks. The emphasis is on creating grounded, reliable agents suitable for production engineering rather than demonstrations that cannot consistently deliver accurate results.

Build CI/CD Validation

Create and maintain automated validation pipelines designed to catch issues such as invalid configurations, leaked credentials, and broken agent contracts before they reach production.

You may also review contributions from engineers across teams, help enforce platform standards, and ensure new capabilities remain robust and cost-effective.

Optimize AI Cost and Value

Track platform costs and evaluate the value generated by different AI workflows. You’ll help make deliberate decisions around model selection, token usage, and whether a particular task should use AI or conventional deterministic software.

Evaluate Emerging AI Technology

Stay current with the rapidly evolving AI tooling ecosystem, evaluate technologies that could improve the platform, and make recommendations based on technical capability, reliability, and cost.

Minimum Qualifications

• B.S. or M.S. in Computer Science, Engineering, or a related discipline, or equivalent practical experience.
• Experience building software, applications, scripts, or services using Python or a comparable general-purpose programming language.
• Ability and willingness to work with Python.
• Understanding of software development fundamentals including version control, testing, debugging, and code review.
• Practical experience developing, deploying, operating, or troubleshooting software in Linux environments.
• Hands-on Model Context Protocol experience implementing, integrating, extending, or operating MCP clients, servers, tools, or MCP-based workflows.
• Ability to explain how MCP has been used to connect an AI system with tools or external systems.
• Hands-on experience building an AI agent or LLM-powered workflow that performs meaningful work using tools, APIs, structured workflows, files, databases, or other external systems.
• Experience that goes beyond basic chatbots, prompt experimentation, or using an AI assistant solely for text generation.
• Experience building or integrating retrieval-augmented generation workflows that ground AI responses in authoritative information such as documentation, source code, databases, or files.

Preferred Experience

Strong candidates may also bring experience across several of the following areas:

• Platform architecture and software system design.
• Shell scripting and workflow automation.
• CI/CD pipelines and automated validation.
• Docker or Podman.
• REST API development and integration.
• FastAPI or similar Python web frameworks.
• GitHub, Jira, Confluence, Microsoft Teams, or comparable APIs.
• PostgreSQL, Redis, or similar data infrastructure.
• Agent evaluation and observability.
• Prompt engineering and model selection.
• Embedded systems and firmware.
• Semiconductors and ASIC development.
• Hardware/software integration.
• Developer tooling and internal platforms.
• Inner-source engineering.
• Microsoft Teams bot development or Power Automate.
• Operating production services or internal developer platforms.

You are not expected to be an expert in every technology listed. Cornelis Networks emphasizes engineering fundamentals, hands-on experience, curiosity, sound technical judgment, and the ability to learn quickly.

Core Technologies and Skills

• Python
• Linux
• AI agents and LLM-powered workflows
• Model Context Protocol (MCP)
• Retrieval-Augmented Generation (RAG)
• FastAPI and REST APIs
• Docker or Podman
• CI/CD and automated validation
• PostgreSQL and Redis
• GitHub, Jira, Confluence, and Microsoft Teams integrations
• Prompt engineering and agent evaluation
• Developer platform engineering

Why This Role Stands Out

This is an emerging engineering discipline, and candidates are not expected to have previously held the exact title of AI Platform Engineer.

The opportunity combines traditional software and platform engineering with practical AI agent development. You’ll be working on AI systems intended to perform useful engineering work inside a technically demanding organization rather than building generic AI demonstrations.

The platform will support engineers working across high-performance networking, firmware, embedded systems, ASIC development, Linux kernel drivers, validation, and hardware/software integration.

Benefits and Compensation

Cornelis Networks offers a total rewards package that can include base compensation, equity, cash incentives, and performance-based incentives depending on the position.

Benefits listed for eligible employees include:

• Medical coverage.
• Dental coverage.
• Vision coverage.
• Disability and life insurance.
• Dependent care flexible spending account.
• Accidental injury insurance.
• Pet insurance.
• 401(k) with company match.
• Generous paid holidays.
• Open Time Off for regular full-time exempt employees.
• Sick time.
• Bonding leave.
• Pregnancy disability leave.

The job posting does not specify a base salary range. Actual base pay is determined using factors including skills, qualifications, experience, and location relative to the applicable hiring range.

Location and Employment Details

This remote opportunity is available to employees residing within the United States.