AI Engineer building scalable applications using AI tools and guiding teams to adopt AI effectively. Innovating engineering culture and workflows in an established Fortune 500 company.
About the role
- Senior AI Engineer designing and building agentic systems that automate complex audit workflows for Fieldguide. Leading technical projects and mentoring team members while delivering high-impact results.
Responsibilities
- Build and Ship AI Agents
- Design and build agentic systems that automate complex audit workflows end-to-end
- Translate customer problems into concrete agent behaviors and orchestration logic
- Orchestrate LLMs, tools, retrieval, and business logic into reliable, production-grade agent experiences
- Own agents across their lifecycle: delivery, reliability, performance, and observability
- Execute with AI-Native Leverage
- Use AI to accelerate design, build, test, and iteration cycles
- Prototype quickly, then harden systems for enterprise-grade reliability
- Build evaluation frameworks, feedback loops, and guardrails to improve agents over time
- Design prompts, retrieval pipelines, and orchestration logic that perform at scale
- Drive Product Impact
- Make clear trade-offs on what to build, cut, or skip based on customer value
- Partner with Product and Design to define capabilities that deliver real outcomes
- Stay close to customer workflows and optimize for highest-impact problems
- Identify capability gaps and unblock team progress proactively
- Mentor and Multiply the Team
- Raise the quality bar through code review, design feedback, and pairing
- Create reusable abstractions, patterns, and tooling that increase team velocity
- Share learnings across the team and establish engineering best practices
Requirements
- 3–6+ years shipping production software in complex, real-world systems
- Strong command of TypeScript, Python, and Postgres
- Shipped LLM-powered features serving real production traffic
- Built retrieval pipelines and agent orchestration systems
- Implemented evaluation frameworks for model outputs and agent behavior
- Worked with vector databases, embedding models, and RAG architectures
- Hands-on experience with modern LLM APIs (OpenAI, Gemini, Anthropic) and agent frameworks
- Comfortable operating in ambiguity and taking responsibility for outcomes
Benefits
- Competitive compensation with equity
- Comprehensive health and wellness benefits
- Flexible time off and work schedules
- Technology reimbursements
- 401(k) plan
- Twice-yearly in-person offsites across the U.S.
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