AI Agent Architect Intern

ATC; United States; On-site; Full time

Job description

We are seeking a skilled AI Developer to design, build, and deploy autonomous AI agents from scratch. This role involves creating intelligent systems that can perceive environments, make decisions, and execute actions in real-world or simulated scenarios. You will leverage machine learning, Python, and specialized frameworks like LangChain and LangGraph to develop scalable AI agents for applications such as automation, robotics, virtual assistants, or multi-agent simulations.

Key Responsibilities

• Architect and implement AI agents from the ground up using frameworks such as LangChain for chaining LLMs and tools, and LangGraph for stateful, graph-based agent workflows, including perception modules (e.g., using computer vision or NLP), decision-making logic (e.g., via reinforcement learning or planning algorithms), and action execution components.
• Develop and train machine learning models using frameworks like TensorFlow, PyTorch, or Scikit-learn to enable agent learning and adaptation, integrating with LangChain/LangGraph for advanced agent orchestration.
• Integrate AI agents with external systems, APIs, databases, and environments (e.g., simulation tools like OpenAI Gym or real-world interfaces), ensuring seamless tool usage and memory management via LangChain components.
• Optimize agents for performance, scalability, and robustness, including handling edge cases, ethical considerations, and safety protocols within graph-structured agent designs.
• Collaborate with cross-functional teams (e.g., data scientists, software engineers) to iterate on agent designs based on feedback and testing.
• Conduct experiments, simulations, and evaluations to refine agent behaviors and ensure reliability in production.
• Document code, architectures, and methodologies for reproducibility and team knowledge sharing.
• Stay current with advancements in AI agent technologies, such as large language models (LLMs), multi-agent systems, and emerging frameworks like LangChain and LangGraph.

Required Skills and Qualifications

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field.
• Proficiency in Python programming, with strong experience in ML libraries (e.g., TensorFlow, PyTorch, NumPy, Pandas) and agent-specific tools (e.g., LangChain, LangGraph, AutoGen, RLlib, Hugging Face Transformers).
• Hands-on experience building AI agents from scratch using LangChain for tool integration and agent chains, LangGraph for multi-step reasoning and state management, including reinforcement learning, state machines, graph-based planning, or evolutionary algorithms.
• Solid understanding of data structures, algorithms, software engineering principles, and version control (e.g., Git).
• Familiarity with cloud platforms (e.g., AWS, Google Cloud, Azure) for deploying agents, and tools like Docker/Kubernetes for containerization.
• Strong problem‑solving skills, with the ability to debug complex systems and work in agile, fast‑paced environments.
• Excellent communication skills to articulate technical designs and collaborate effectively.

Preferred Qualifications

• Experience with specialized domains like natural language processing (NLP), computer vision, robotics (e.g., ROS), or game AI, integrated with LangChain/LangGraph.
• Knowledge of big data tools (e.g., Spark, Hadoop) or databases (SQL/NoSQL) for handling large‑scale agent data.
• Prior work with multi‑agent systems, ethical AI, or real‑time applications using advanced frameworks.
• Contributions to open‑source AI projects or a portfolio demonstrating agent‑building expertise (e.g., GitHub repos showcasing LangChain/LangGraph implementations).