Asks for

The ad says“5+ years of production software engineering experience, with 2+ years focused on bringing LLM-based applications or agent systems to market”
The ad says“Demonstrated proficiency using AI coding tools (Cursor, Copilot, Claude, etc.) to accelerate development”
The ad says“Experience integrating LLM-powered features with external APIs, databases, and third-party tools”
The ad says“Hands-on experience building and deploying agentic systems using frameworks such as LangChain/LangGraph, CrewAI, AutoGen, or custom orchestration”
The ad says“Experience designing and operating background job / async task pipelines (Celery, RQ, Temporal, or similar) for long-running agent runs and reliable retries”
The ad says“Experience designing and operating background job / async task pipelines (Celery, RQ, Temporal, or similar) for long-running agent runs and reliable retries”
The ad says“Strong understanding of LLM fundamentals: prompt engineering, function/tool calling, retrieval-augmented generation (RAG), context window management, and token economics”
The ad says“Hands-on experience building and deploying agentic systems using frameworks such as LangChain/LangGraph, CrewAI, AutoGen, or custom orchestration”
The ad says“Experience integrating LLM-powered features with external APIs, databases, and third-party tools”
The ad says“Strong understanding of LLM fundamentals: prompt engineering, function/tool calling, retrieval-augmented generation (RAG), context window management, and token economics”
The ad says“Hands-on experience building and deploying agentic systems using frameworks such as LangChain/LangGraph, CrewAI, AutoGen, or custom orchestration”
The ad says“Hands-on experience building and deploying agentic systems using frameworks such as LangChain/LangGraph, CrewAI, AutoGen, or custom orchestration”
The ad says“5+ years of production software engineering experience, with 2+ years focused on bringing LLM-based applications or agent systems to market”
The ad says“Experience building multi-agent systems with planning, delegation, and inter-agent communication patterns”
The ad says“Strong understanding of LLM fundamentals: prompt engineering, function/tool calling, retrieval-augmented generation (RAG), context window management, and token economics”
The ad says“Proficiency with Python in production environments”
The ad says“Strong understanding of LLM fundamentals: prompt engineering, function/tool calling, retrieval-augmented generation (RAG), context window management, and token economics”
The ad says“Experience designing and operating background job / async task pipelines (Celery, RQ, Temporal, or similar) for long-running agent runs and reliable retries”
The ad says“Experience designing and operating background job / async task pipelines (Celery, RQ, Temporal, or similar) for long-running agent runs and reliable retries”
The ad says“Strong understanding of LLM fundamentals: prompt engineering, function/tool calling, retrieval-augmented generation (RAG), context window management, and token economics”

About Re:Build

At Re:Build, our mission is to ensure the next generation of important products are made, at scale, in America. We are laying the foundation for a better future for our customers, employees, and communities by revitalizing America's manufacturing base and creating meaningful jobs across the country, including in historically deindustrialized regions.

We operate an advanced, end-to-end manufacturing platform that partners with industrial companies and innovators to take products from first concept to full-scale production in critical verticals including aerospace and defense, electrification, medical, energy and environment, and robotics and automation.

The way we operate is as important as the work we do. It's guided by The Re:Build Way, 16 principles that shape how we collaborate with each other, partner with our customers and vendors, and contribute to the communities where we operate. (link to The Re:Build Way principles)

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.

This is an early role for a hands-on AI engineer to design and build the agents at the core of our platform, backed by a parent company with deep roots in engineering and manufacturing. You'll own the AI systems that set our product apart: agents that understand hardware workflows, anticipate problems, and take action on behalf of engineering teams.

Who We're Looking For

We're looking for an AI engineer who works equally well in applied research and production software. You've shipped LLM-powered agent systems to real users, you have strong intuitions about prompt engineering, tool use, and orchestration patterns, and you keep up with a field that changes fast. You're comfortable evaluating new frameworks, models, and techniques on short cycles and making pragmatic build-vs.-adopt decisions as the landscape shifts.

We currently use LangChain's DeepAgent framework for our agent orchestration, but every layer of our AI stack is open to change. The right person for this role understands how agent systems work under the hood, not just one framework's abstractions, and can adapt as better tools and approaches emerge.

This senior engineer will work alongside our engineering team and head of product to build, optimize, and operate the AI agents that give users proactive coordination, risk surfacing, status summaries, and AI-generated deliverables. The role is hands-on. You'll write code daily while contributing to AI architecture decisions and helping define how we evaluate and evolve our agent capabilities over time.

What You'll Do

Your day-to-day responsibilities will include:

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

Technical Requirements

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
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

Highly Valuable:

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

What We Offer

Real Impact: The opportunity to build purpose-built tooling for an entire industry that has never had it

Customer Access: Direct exposure to hundreds of real hardware projects annually through Re:Build's engineering and manufacturing companies

Technical Growth: Hands-on work at the frontier of applied AI, solving novel agent design challenges with real-world feedback loops

Autonomy: Backed by Re:Build while operating with startup independence

Benefits: Full health/dental/vision, bonus program, generous 401K, paid time off, annual learning stipend

Equity & Growth: Participation in Re:Build's LTIP equity program and opportunity for founder equity in potential spin-out

Compensation & Location

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

The base salary range for this position is $143,666.72 - $215,500.08 per year. This range represents the Company’s good faith estimate of the base compensation for this role at the time of posting. Actual compensation will be determined based on several factors, including but not limited to relevant experience, skills, qualifications, internal equity, and geographic location. In addition to base salary, this role may be eligible for annual incentive compensation and/or long-term incentives, subject to Company plans. The Company offers a comprehensive benefits package including medical, dental, vision, retirement, paid time off, and other benefits.

Please note this role is restricted to US citizens or lawful permanent residents only due to exposure to export controlled information.

We want to work with people that reflect the communities in which we operate

Re:Build Manufacturing is proud to be an Equal Employment Opportunity and Affirmative Action employer. We do not discriminate based upon race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, veteran status, marital status, parental status, cultural background, organizational level, work styles, tenure and life experiences. Or for any other reason.

Re:Build is committed to providing reasonable accommodations for qualified individuals with disabilities in our job application procedures. If you need assistance or an accommodation due to a disability, you may contact us at accommodations.ta@ReBuildmanufacturing.com or you may call us at 617.909.6275.