Agentic AI Engineering Intern

Leidos

About The Position

The Leidos Chief Data & Analytics Office (CDAO) is a high-growth organization at the center of the company's technology strategy. Our Operational AI (Ops AI) division is seeking a motivated and talented Agentic AI Engineering Intern to join our team for the summer of 2026. This internship is designed to provide hands-on experience in the development and deployment of cutting-edge generative AI solutions that enhance decision intelligence and insights across mission-critical applications. As an Agentic AI Engineering Intern, you will work alongside experienced principal engineers, data scientists, and systems architects to contribute to real-world projects that are shaping the future of intelligent autonomous AI systems. You will gain exposure to the full lifecycle of agentic AI, from design and prototyping to deployment and monitoring, while building foundational skills in one of the fastest-growing areas of artificial intelligence. This is an exceptional opportunity for an aspiring AI professional who is eager to learn, build, and contribute to scalable, high-performance, and trusted AI systems that help accelerate innovation and improve mission outcomes.

Requirements

Currently pursuing a Bachelor's degree (rising junior or senior preferred) in Computer Science, Artificial Intelligence/Machine Learning, Engineering, or a closely related quantitative field.
US citizenship required.
Proficiency in Python and familiarity with at least one major ML library or framework (e.g., TensorFlow, PyTorch, Scikit-learn, or Hugging Face Transformers).
Basic understanding of the machine learning lifecycle, including data preparation, model training, evaluation, and deployment concepts.
Demonstrated interest in agentic AI patterns, multi-agent systems, and/or LLM-based workflows (e.g., through coursework, personal projects, or research).
Foundational understanding of cybersecurity principles as they relate to AI systems.
Familiarity with version control systems (e.g., Git/GitHub).
Strong analytical, problem-solving, and communication skills.
Ability to work collaboratively in a team-oriented environment.

Nice To Haves

Experience with Retrieval-Augmented Generation (RAG) architectures or vector database technologies (e.g., Pinecone, Weaviate, ChromaDB, FAISS).
Exposure to MLOps platforms or workflows (e.g., MLflow, Kubeflow, or cloud-native ML services).
Familiarity with containerization technologies (e.g., Docker) and basic orchestration concepts (e.g., Kubernetes).
Hands-on experience with a major cloud platform (AWS, Azure, or GCP).
Experience with or coursework in prompt engineering, LLM fine-tuning, or agent framework development (e.g., LangChain, LangGraph, CrewAI, AutoGen).
Familiarity with the Model Context Protocol (MCP) or similar standards for LLM tool integration.
Knowledge of AI ethics, responsible AI practices, or federal AI compliance standards (e.g., NIST AI RMF).
Knowledge of AI security frameworks such as MITRE ATLAS.
Contributions to open-source AI/ML projects or a public portfolio of AI-related work (e.g., GitHub, Kaggle).
Prior internship, co-op, or research experience in a national security, defense, or intelligence community environment.

Responsibilities

Agentic AI Systems Development: Assist in the development and testing of agentic AI systems, including Multi-Agent and Agent-to-Agent (A2A) workflows, leveraging common industry standards such as the Model Context Protocol (MCP) to create interoperable AI agents.
LLM Tool Implementation: Support the implementation of MCP Tools and Resources that enable Large Language Models (LLMs) to interact with internal systems and APIs in a secure, standardized manner.
AI Gateway Contribution: Collaborate with engineers and data scientists to contribute to the architecture of a centralized "AI Gateway" that provides a unified, platform-independent interface for leveraging various LLMs.
Observability & Monitoring: Help implement observability pipelines to track trace-level data, monitor model latency, and support the optimization of Generative AI systems in production.
Production-Ready Solutions: Work closely with senior team members to translate strategic designs into functional, production-ready solution components.
AI Guardrails & Security: Participate in the implementation of AI guardrails to filter inputs and outputs, supporting data security, integrity, and the prevention of adversarial attacks such as prompt injection.
RAG Architecture Support: Assist in the design and implementation of Retrieval-Augmented Generation (RAG) pipelines to enhance LLM accuracy and grounding with enterprise data sources.
Software Engineering Best Practices: Learn and apply engineering best practices including version control (Git), automated testing, and CI/CD processes for AI systems.
Research & Innovation: Stay current with emerging trends in agentic AI, operational AI, and MLOps, and contribute ideas to continuously evolve the team's capabilities.