Senior AI Engineer

About the role

The Opportunity: If you've worked alongside hardware teams, you know the damage that results from a missed change request or critical context that was never relayed to the right person. Reflow exists to close that gap. We're building the first AI-powered platform built for hardware product development, one that listens across the tools teams already use, maintains a structured picture of every program, and proactively coordinates across disciplines when things inevitably change.

Responsibilities

• Designing, building, and iterating on LLM-powered agents that coordinate across engineering disciplines, surface project risks, and generate structured deliverables (proposals, SOWs, status reports)
• Owning the agent orchestration layer (currently LangChain DeepAgent) and continuously evaluating whether to extend, replace, or supplement it as new frameworks and patterns emerge
• Implementing robust tool-use patterns that connect agents to external systems (project management tools, CAD/PLM platforms, communication channels) via APIs and integrations
• Designing and tuning prompts, chains, and retrieval strategies to maximize agent reliability, accuracy, and usefulness across diverse hardware project contexts
• Building evaluation and observability infrastructure for agent performance, including tracing, cost tracking, latency monitoring, and automated quality benchmarks
• Developing streaming agent interfaces that surface real-time progress, reasoning transparency, and proactive alerts to end users
• Staying current with rapid advances in LLMs, agent frameworks, and related tooling, and translating that awareness into actionable recommendations for the team
• Collaborating with frontend engineers on the UX of AI-powered features and with backend engineers on data pipelines and API design
• Contributing to AI architecture decisions, code reviews, and engineering best practices

Requirements

• 5+ years of production software engineering experience, with 2+ years focused on bringing LLM-based applications or agent systems to market
• Demonstrated proficiency using AI coding tools (Cursor, Copilot, Claude, etc.) to accelerate development
• Strong understanding of LLM fundamentals: prompt engineering, function/tool calling, retrieval-augmented generation (RAG), context window management, and token economics
• Experience integrating LLM-powered features with external APIs, databases, and third-party tools
• Experience designing and operating background job / async task pipelines (Celery, RQ, Temporal, or similar) for long-running agent runs and reliable retries
• Experience building multi-agent systems with planning, delegation, and inter-agent communication patterns
• Demonstrated ability to evaluate and adopt new AI tools and frameworks quickly, with a track record of staying ahead of a fast-moving field
• Highly Valuable:
• Direct experience with LangChain's DeepAgent or LangGraph for multi-step agent orchestration
• Experience with evaluation frameworks for LLM outputs (automated scoring, human-in-the-loop evaluation, regression testing for prompts)
• Familiarity with vector databases and embedding pipelines (Pinecone, Weaviate, pgvector, or similar)
• Experience with model serving infrastructure, fine-tuning workflows, or model selection/routing strategies
• Understanding of authentication/authorization patterns (OIDC, JWT) and secure handling of user data in LLM contexts
• Experience in hardware development, engineering workflows, or project management concepts (phases, gates, dependencies, requirements traceability)
• TypeScript / React fluency, enough to pair with frontend engineers on streaming agent UIs and reasoning-transparency surfaces

Qualifications

• Must Have:
• 5+ years of production software engineering experience, with 2+ years focused on bringing LLM-based applications or agent systems to market
• Demonstrated proficiency using AI coding tools (Cursor, Copilot, Claude, etc.) to accelerate development
• Strong understanding of LLM fundamentals: prompt engineering, function/tool calling, retrieval-augmented generation (RAG), context window management, and token economics
• Experience integrating LLM-powered features with external APIs, databases, and third-party tools
• Experience designing and operating background job / async task pipelines (Celery, RQ, Temporal, or similar) for long-running agent runs and reliable retries
• Experience building multi-agent systems with planning, delegation, and inter-agent communication patterns
• Demonstrated ability to evaluate and adopt new AI tools and frameworks quickly, with a track record of staying ahead of a fast-moving field

Skills

• Python in production environments
• Experience with model serving infrastructure, fine-tuning workflows, or model selection/routing strategies
• Understanding of authentication/authorization patterns (OIDC, JWT) and secure handling of user data in LLM contexts
• Experience in hardware development, engineering workflows, or project management concepts (phases, gates, dependencies, requirements traceability)
• TypeScript / React fluency, enough to pair with frontend engineers on streaming agent UIs and reasoning-transparency surfaces

Benefits

• Full health/dental/vision
• Bonus program
• Grossly generous 401K
• Paid time off
• Annual learning stipend
• Participation in Re:Build's LTIP equity program and opportunity for founder equity in potential spin-out
• Potential equity stake under independent spinout scenario

Pay

Base salary range of $143,000 to $215,000 with performance bonus and long-term incentive plan offered. Potential equity stake under independent spinout scenario.

Schedule

Remote-first, with preference for candidates in Boston, Seattle, Los Angeles, or other cities with Re:Build offices