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.
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.
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.
Help implement observability pipelines to track trace-level data, monitor model latency, and support the optimization of Generative AI systems in production.
Work closely with senior team members to translate strategic designs into functional, production-ready solution components.
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.
Assist in the design and implementation of Retrieval-Augmented Generation (RAG) pipelines to enhance LLM accuracy and grounding with enterprise data sources.
Learn and apply engineering best practices including version control (Git), automated testing, and CI/CD processes for AI systems.
Stay current with emerging trends in agentic AI, operational AI, and MLOps, and contribute ideas to continuously evolve the team's capabilities.

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.

AI Engineering Intern

Key skills