AI Developer

Job Description We’re looking for a teammate with: The ideal candidate will have hands-on experience building and shipping agentic AI systems, with deep proficiency in Python and the modern AI engineering stack: • Python Expertise: Python is your primary language. You write clean, production-grade code, build robust data pipelines, and are comfortable owning the full lifecycle from prototype to deployment. • Agentic Frameworks: Practical experience with LangGraph, LangChain, AgentCore, or comparable multi-agent orchestration frameworks — including agent routing, state management, tool use, memory, and evaluation. • LLM Integration: Demonstrated experience integrating large language models via AWS Bedrock, Anthropic APIs, or similar — including prompt engineering, context management, and output validation. • Data Source Integrations: Hands-on experience connecting applications to modern data platforms including Snowflake, Apache Iceberg, OpenSearch, or similar. • MCP / Tool-Use Patterns: Familiarity with Model Context Protocol (MCP) or equivalent patterns for giving agents structured, governed access to external data systems. • API Development: Experience building production REST APIs using FastAPI or comparable Python frameworks. • Cloud Platforms: Hands-on AWS experience, with Bedrock exposure strongly preferred. Familiarity with IAM, Lambda, and API Gateway a plus.

Beyond technical skills, we’re looking for someone who brings: • Bias for Action: You ship, instrument, and iterate — you don’t wait for perfect requirements. • Strong Communication: Ability to translate complex agentic system behavior to non-technical stakeholders clearly and concisely. • Security and Governance Mindset: Awareness of responsible AI practices, data privacy considerations, and the importance of auditability in agentic systems. • Collaborative Spirit: Comfortable working across functions and levels, from CSMs to the C-suite.

Job Responsibilities Here’s how you’ll be making an impact: • Build Multi-Agent Pipelines: Design and implement orchestrated multi-agent systems using LangGraph and AgentCore, including routing logic, evaluation loops, retry mechanisms, and agent specialization patterns. • Develop with LangChain: Leverage LangChain to build sophisticated prompt pipelines, tool-augmented agents, memory constructs, and retrieval-augmented generation workflows. • Deploy on AWS Bedrock: Leverage AWS Bedrock to host, invoke, and manage LLM-powered agents at scale, ensuring reliability, cost efficiency, and security compliance. • Build FastAPI Services: Develop lightweight, production-ready API services that expose agentic capabilities to downstream consumers and orchestration platforms. • Iterate Rapidly: Operate in a fast-moving incubator environment — prototype quickly, instrument your work, and evolve solutions based on real usage signals. • Collaborate Cross-Functionally: Partner closely with Customer Success, Sales, and Analytics stakeholders to translate business requirements into agentic architectures.