Junior GenAI Developer Intern - Summer 2026

CACI•Reston, VA

About The Position

We’re seeking a Junior GenAI Developer passionate about creating and delivering AI-driven solutions—from crafting effective prompts and integrating models to building evaluation frameworks and implementing safety guardrails. In this role, you’ll collaborate closely with senior engineers and product managers to prototype, refi ne, and deploy GenAI capabilities that make a real difference for our customers.

Requirements

Bachelor’s in CS/Engineering or equivalent experience (internships count).
0–2 years building soft ware(internships count).
Hands-on with at least one: Python (FastAPI, Flask) or TypeScript/Node (Express, Next.js).
Familiarity with LLM APIs (OpenAI/Azure OpenAI/Anthropic) and prompt engineering basics.
Experience with Git, RESTAPIs, and CI/CD concepts.
Basic knowledge of cloud (AWS desired, Azure acceptable) and containers (Docker).
Strong communication and curiosity
Must be legally authorized to work in the United States without the need for employer sponsorship, now or at any time in the future

Nice To Haves

Exposure to LangChain, LangGraph, Ango, and Langfuse
Exposure to OpenWebUI
Understanding of RAG concepts and vector databases.
Familiarity with GenAI models

Responsibilities

Prototype and integrate LLMs (OpenAI, Azure OpenAI, Anthropic, etc.) into web services, apps, and workflows.
Support the design and development of intelligent chatbots and autonomous agents using cutting-edge GenAI technologies to support content analysis, content gap identification, content summarization and like activities.
Implement and optimize RAG (Retrieval Augmented Generation) systems to provide accurate information retrieval and contextual responses
Write prompt chains, system prompts, and structured outputs (JSON/XML/Markdown) for reliable responses.
Build evaluation harnesses: test sets, metrics, and A/B experiments.
Develop, manage, and deploy intelligent AI agents to include knowledge retrieval (RAG), allowing interaction with external APIs, databases, and applications.
Develop connectors and RAG pipelines.
Use open-source platforms for debugging, testing, testing, and monitoring generative AI applications
Implement guardrails and safety (content and safety filters, PII scrubbing, hallucination detection, policy enforcement).
Develop, manage, and deploy intelligent AI agents to include knowledge retrieval (RAG), allowing interaction with external APIs, databases, and applications.
Develop connectors and RAG pipelines.
Contribute to backend services (Python/TypeScript) and micro-APIs to operationalize AI features.
Write observability hooks: logging, tracing, prompt/version tracking, feedback loops.
Collaborate on model selection and cost/performance tradeoff s (latency, throughput, token cost, quality).
Document prompt patterns, playbooks, and API usage; share learnings in reviews/demos.