Senior AI Engineer

Re:Build Manufacturing•Los Angeles, MA

1d•Remote

About The Position

Reflow is building an AI-powered platform for hardware product development. This platform aims to improve communication and coordination between hardware teams by listening across existing tools, maintaining program structure, and proactively coordinating changes. This is an early-stage role for a hands-on AI engineer to design and build the core agents of the platform. The role involves owning AI systems that understand hardware workflows, anticipate problems, and act on behalf of engineering teams. The ideal candidate has experience in both applied research and production software, has shipped LLM-powered agent systems, possesses strong intuitions about prompt engineering, tool use, and orchestration patterns, and stays current with the rapidly evolving AI field. They should be comfortable evaluating new frameworks, models, and techniques, and making pragmatic build-vs.-adopt decisions. The role is hands-on, requiring daily coding, contributions to AI architecture, and defining evaluation methods for agent capabilities. The Senior AI Engineer will work with the engineering team and head of product to build, optimize, and operate AI agents that provide proactive coordination, risk surfacing, status summaries, and AI-generated deliverables.

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
Hands-on experience building and deploying agentic systems using frameworks such as LangChain/LangGraph, CrewAI, AutoGen, or custom orchestration
Strong understanding of LLM fundamentals: prompt engineering, function/tool calling, retrieval-augmented generation (RAG), context window management, and token economics
Proficiency with Python in production environments
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
Strong software engineering fundamentals: clean architecture, testing, version control, and code review practices
Ability to balance rapid experimentation with production-grade reliability

Nice To Haves

Direct experience with LangChain's DeepAgent or LangGraph for multi-step agent orchestration
Background in 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
Background in B2B SaaS platforms, project management tools, or technical collaboration products
Familiarity with 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

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