- Design and Development : Lead the design and implementation of sophisticated AI agents, including multi-agent systems, utilizing frameworks such as LangChain, LangGraph, Strands API, or other relevant agentic frameworks.

- Prompt Engineering Expertise : Serve as the subject matter expert in prompt engineering, developing and optimizing prompts for Large Language Models (LLMs) to achieve high-fidelity, reliable, and desired agent behaviors.

- Agent Systems Architecture : Architect and build production-ready agent workflows, ensuring scalability, reliability, and maintainability.

- Cloud Deployment : Deploy and manage AI solutions using AWS Bedrock and the broader AWS serverless ecosystem (Lambda, API Gateway, DynamoDB, IAM, S3, etc.).

- Problem Solving & Pitfalls : Apply deep knowledge of the pitfalls and challenges inherent in modern AI solutions (e.g., grounding, hallucinations, bias, security) to design robust mitigation strategies.

- Collaboration & Communication : Collaborate effectively with cross-functional teams (product managers, data scientists, and engineers) and clearly articulate technical concepts, progress, and challenges through excellent verbal and written communication.

- Code Quality : Ensure high standards for code quality, testing, and documentation for all agent-based systems.

Required Qualifications :

- Programming Proficiency : Expert-level proficiency in Python.

- Agent Frameworks : Extensive hands-on experience with modern AI agent development frameworks (e.g., LangChain, LangGraph, Strands API).

- Prompt Engineering : Demonstrated expertise in advanced prompt engineering techniques, including few-shot learning, chain-of-thought prompting, tool use, and adversarial prompt defense.

- AI Architecture : Proven experience building and deploying a wide variety of agents, especially complex multi-agent systems, in production environments.

- Cloud Expertise : Solid experience with AWS Bedrock and significant practical experience with core AWS serverless technologies (Lambda, API Gateway, DynamoDB, IAM, S3, etc.).

- Communication : Excellent verbal and written communication skills are essential for technical documentation, cross-team collaboration, and presenting solutions.

Preferred (Bonus) Qualifications :

- Machine Learning Foundation : Knowledge of traditional machine learning techniques such as regression, classification, clustering, deep learning, reinforcement learning, etc.

- Mathematical Background : A strong mathematical background that underpins an understanding of ML and AI algorithms.

- Working Hours : The expected working hours will require partial overlap with Phoenix morning till noon hours (MST) to facilitate effective team collaboration.