AI Platform Engineer

Cornelis Networks

AI Platform Engineer at Cornelis Networks creating a private, secure AI platform for engineering workflows. Focus on automating meaningful work with AI agents and enhancing engineering efficiency.

Posted 7/25/2026full-timeRemote • Texas • 🇺🇸 United StatesMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in AI platform management, including the design and implementation of autonomous agents and workflows. Proficient in Python programming, Linux environments, and integrating AI systems with external tools and databases.

Highest-signal resume keywords

Python ProgrammingAI Agent DevelopmentLinux Environment ExperienceMCP IntegrationRAG Workflow Implementation

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Software Development FundamentalsVersion ControlTestingDebuggingCode ReviewFastAPI REST APICLI Interface DevelopmentPrompt EngineeringCI/CD Validation PipelinesContainerization

Tools & Technologies

PostgreSQLRedisGitHubJiraConfluenceMicrosoft Teams

Industry Keywords

AI PlatformAutonomous AgentsEngineering OperationsCost ManagementModel Selection

Tech Stack

Tools & technologies

LinuxPostgresPythonRedis

About the role

Key responsibilities & impact

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.
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.
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.
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.
Track the AI Landscape and Keep the Platform Current: Evaluate what matters, adopt what improves the platform, and upgrade before the team falls behind.

Requirements

What you’ll need

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
Practical experience developing, deploying, operating, or troubleshooting software in a Linux environment
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
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 building or integrating a RAG workflow that grounds model output in documentation, code, databases, files, or other authoritative information

Benefits

Comp & perks

health and retirement benefits
equity, cash, and incentives
medical, dental, and vision coverage
disability and life insurance
dependent care flexible spending account
accidental injury insurance
pet insurance
generous paid holidays
401(k) with company match
Open Time Off (OTO) for regular full-time exempt employees
other paid time off benefits include sick time, bonding leave, and pregnancy disability leave