Senior AI Integration Developer

Senior Ai Integration Developer

Peraton Labs is seeking a Senior AI Integration Developer to lead the design and implementation of an AI assistant capability within an existing web application in support of RF spectrum monitoring for the Department of Defense. This is a technically demanding role at the intersection of applied AI, software engineering, and operational tooling.

The core focus of this position is the development of a context-aware AI assistant and the Model Context Protocol (MCP) server and tooling infrastructure that connects it to the application's data, workflows, and services. Given the sensitive nature of the operational environment, the primary deployment target is locally-hosted models (e.g., Ollama) running in air-gapped or connectivity-constrained environments — with cloud-based LLM APIs as a secondary consideration.

The right candidate understands not just how to wire up a model, but how to design tool interfaces and select or tune models that perform reliably under these constraints. It is particularly important for the candidate to take the time to properly understand the application domain and CONOPs in order to develop appropriate MCP tool chains.

This individual will work closely with the broader engineering team and domain stakeholders to identify high-value AI use cases, implement and iterate on MCP tools, and evaluate and improve the quality of AI-generated outputs over time. Familiarity with the full stack is also expected, as effective AI integration requires understanding the existing system that the assistant will interact with. The core web application for this effort uses the following technologies in the stack: FastAPI backend, React frontend, and PostgreSQL database).

Key responsibilities may include:

• Design and implement MCP server and tool interfaces that expose application data and functionality to the AI assistant
• Deploy and configure locally-hosted models (e.g., Ollama) for use in air-gapped or connectivity-constrained environments
• Evaluate and select local models appropriate for specific assistant tasks; assess capability and performance tradeoffs across model sizes and families
• Integrate LLM inference endpoints into the application backend and frontend, supporting both local and cloud-hosted models where applicable
• Develop and refine system prompts, tool definitions, and context management strategies optimized for the capabilities and limitations of local models
• Define and execute evaluation frameworks to assess AI output quality, tool call accuracy, and assistant reliability
• Identify high-value use cases in collaboration with domain experts and stakeholders; translate them into concrete AI tool designs
• Maintain and extend backend Python and Type Script/Node.js services supporting AI functionality or work closely with other engineers to do so
• Document AI architecture, tool schemas, prompt strategies, model configurations, and evaluation results
• Stay current with the evolving local model and MCP ecosystem landscape

Required Qualifications:

• Minimum of 8 years of experience with a Bachelor's degree; 6 years with a Master's degree; or 3+ years with a PhD in Computer Science, Computer Engineering, Information Systems, or similar/related programs.
• Experience deploying and working with locally-hosted models (e.g., Ollama, llama.cpp, or similar) in offline or restricted network environments
• Strong understanding of the Model Context Protocol (MCP) — server design, tool schemas, and client-server communication
• Experience with prompt engineering and system prompt design, particularly tuning prompts for the capabilities of smaller or quantized local models
• Experience with agentic AI patterns — multi-step reasoning, tool chaining, and error recovery
• Familiarity with model selection tradeoffs — capability, context length, quantization, and hardware requirements
• Ability to design structured evaluation approaches for AI output quality and tool performance
• Strong judgment about AI assistant UX — what makes a tool call well-designed, when an AI response is actually useful, etc.
• Proficiency in Python; familiarity with FastAPI or comparable frameworks
• Experience with…