Description

• Experience: 8+ years implementing ML/AI technologies, with at least 2 years focused on building and scaling LLM-driven applications.
• Agentic Mastery: Proven track record of building and deploying agentic workflows. Deep expertise in orchestration frameworks (e.g., LangGraph).
• Advanced Prompt Engineering & Tool-Use: Expertise in teaching models to use external APIs, databases, and legacy tools effectively. You should have a deep understanding of tool-calling, structured output generation, and context window management.
• Domain Expertise: Demonstrated experience taking AI models from notebooks to high-availability environments within highly regulated industries (e.g., P&C Insurance, Banking, or Healthcare).
• Technical Proficiency: Advanced Python skills; deep familiarity with state-of-the-art LLMs (OpenAI, Anthropic). Experience with graph-based data structures is a plus.
• Cloud Native Agent Infrastructure: Strong experience with AWS or similar cloud providers, with a focus on building scalable, event-driven architectures that support asynchronous agent tasks.
• Problem Solving: A creative builder mindset capable of architecting solutions in the face of the ambiguity inherent in greenfield agentic projects.
• Communication: Exceptional ability to explain how and why an agent made a specific decision to executive stakeholders, maintaining transparency in autonomous systems.
• Demonstrated ability to embrace AI and apply it to your current role as well as data-driven insights to drive innovation, productivity, and continuous improvement

Key Skills

Education

Bachelor's degree