Deployed Engineer (Raleigh)
Deployed Engineer partners with customer teams to co-build, deploy, and operate production AI agents using LangChain/LangGraph. Owns technical wins in pre-sales POCs/evaluations and provides post-sale architecture advice, demos, and best practices. Requires 3+ years technical experience, strong Python/JS, and hands-on agent design beyond basic APIs.
About the job
Responsibilities
- Co-architect and co-build production AI agents with customer engineering teams.
- Own the technical win in pre-sales by designing POCs, answering deep technical questions, and guiding evaluations.
- Help customers deploy and operate agent-based applications such as conversational agents, research agents, and multi-step workflows.
- Advise customers post-sale on architecture, best practices, and roadmap-level decisions.
- Run technical demos, trainings, and workshops for developer audiences.
- Surface field feedback and contribute reusable patterns, cookbooks, and example code that scale across customers.
- Occasionally contribute code upstream when it meaningfully improves customer outcomes.
Requirements
- 3+ years in a relevant technical role (software engineering, customer engineering, solutions engineering, founding/product engineering), ideally in a startup or scale-up.
- Strong Python, JavaScript and systems fundamentals.
- Have designed agent-based or LLM-powered applications beyond simple API calls, including multi-step workflows, orchestration, and failure handling.
- Comfortable working directly with customers during POCs, architecture reviews, and technical evaluations.
- Can explain technical tradeoffs clearly and build trust with developer audiences.
- Take responsibility for outcomes, not just recommendations.
- Bias toward action and enjoy figuring things out as you go.
- Excited about operating AI agents in production, not just building demos.
Nice-to-Haves
- Deployed AI agents in production, especially using LangChain, LangGraph, or similar frameworks.
- Worked with LLM evaluation, observability, or guardrails.
- Experience with cloud environments (AWS, GCP, Azure), containers, and basic Kubernetes concepts.
- Shipped and operated production software and comfortable owning systems under real-world constraints.
Compensation
- Annual OTE range: $150,000–$250,000 USD.
- Competitive compensation that includes base salary, variable compensation, meaningful equity, benefits, and perks. Actual compensation varies based on role, level, and location.
- Benefits include medical, dental, and vision coverage, flexible vacation, a 401(k) plan, meals on in-office days in the US and more.
Skills
Python, JavaScript, LangChain, LangGraph, LLMs, AWS, GCP, Azure, Kubernetes, Llm Evaluation, Observability, Guardrails
Similar jobs
Solutions Architecture jobs
Blee
United States
Owns end-to-end enterprise implementations from contract to go-live, managing stakeholder mapping, project plans, technical integrations like SSO/APIs/security, and handoffs to CSMs for large legal/marketing teams. Requires 3-5 years in SaaS implementation or consulting with technical fluency and hybrid NYC/SF presence.
Redis
United States
Develop Redis-powered solutions for enterprise customers, lead technical evaluations and pilots, and partner with Sales on account strategy. Requires 3+ years of pre-sales engineering experience, server-side development and Linux expertise, distributed-systems experience, and strong communication skills.
Vitalize
San Francisco, CA
Leads on-site deployments for hospital staffing software, mapping workflows, driving adoption across clinical and ops teams, and building scalable rollout playbooks to activate revenue and ROI.
SnapLogic
United States
Advises enterprise customers on AI adoption by identifying use cases, building prototypes, proving business value, and guiding deployment strategy. Requires hands-on customer advisory experience, strong executive communication, enterprise integration knowledge, and solid AI/LLM expertise.
LangChain
San Francisco, CABuild and deliver production AI agent systems for enterprise customers through architecture advising, co-development, and embedded engineering engagements. Requires 4+ years of software engineering experience, deep Python expertise, and 2+ years shipping production agent systems.