Senior AI Engineer

Key Responsibilities

• Agentic System Design: Design, build, and maintain production-grade agentic systems, multi-agent orchestration, specialist agents, and Human-in-the-Loop workflows, with context management, memory, and tool-calling.
• MCP & Tool Integration: Develop MCP servers and tool integrations connecting AI agents to Teak's platform APIs and partner systems.
• LLM Integration & Output Control: Orchestrate LLMs (Claude, GPT, Gemini, or similar) across the AI layer, selecting the right model per task and managing prompt and system-prompt strategies so agents present refund solutions in pre-approved, compliant language.
• RAG & Knowledge Systems: Build RAG pipelines that ground agent responses in Teak's policy and product data, with versioning and isolation that prevent hallucination on compliance-sensitive topics.
• Compliance & Safety: Implement guardrails that enforce compliant offer language, prevent unauthorized coverage claims, and meet regulatory requirements across Teak's markets.
• Evaluation: Build evaluation frameworks to monitor, test, and continuously improve agent performance, reliability, and output quality.
• AI Infrastructure & Observability: Build and operate AI infrastructure on AWS, with structured logging and tracing for auditability and rapid issue resolution.
• Backend Development: Contribute to backend services, APIs, and platform improvements in Python alongside AI work, applying clean code, testing, and strong engineering fundamentals.
• Collaboration & Code Quality: Participate in code reviews, document agent system design and integration patterns, take part in Agile workflows, and join the on-call rotation.

Qualifications

• Bachelor's Degree in Computer Science, Engineering, a related field, or equivalent practical experience.
• 5+ years of professional software engineering experience, with at least 2 years focused on production AI/LLM application development.
• Strong Python proficiency and solid software engineering fundamentals.
• Deep backend engineering experience, designing, building, and operating production services and APIs (Django or similar framework), with strong fundamentals in testing, code quality, and system design.
• Hands-on experience building and deploying agentic AI systems (LangGraph, LangChain, AWS Bedrock Agents, or similar).
• Experience building and maintaining RAG pipelines and vector search systems.
• Experience integrating LLM APIs (Anthropic, OpenAI, or similar) into production applications.
• Working knowledge of Model Context Protocol (MCP) and tool-calling patterns.
• Experience designing and running LLM evaluation frameworks for quality, reliability, and safety.
• Experience building and operating AI infrastructure on a major cloud platform (AWS, GCP, or Azure).
• Strong written and verbal communication, with the ability to explain AI system design to technical and non-technical stakeholders.

Bonus Skills

• Background in a compliance-sensitive industry (insurance, fintech, legal) where regulated AI output and guardrails matter.
• Experience operating LLM systems at high volume, with attention to latency, cost, and caching.
• Familiarity with AWS Bedrock or similar managed AI platforms and agent runtimes.
• Experience with model fine-tuning, customization, or distillation.
• Contributions to open-source AI tooling or frameworks.