Deployed Engineer, Professional Services

LangChain builds open-source frameworks and platforms for building, evaluating, deploying, and operating intelligent agents at scale. The Deployed Engineer will work directly with enterprise customers to design, build, evaluate, and deploy reliable production agent systems, providing technical guidance or embedding with customer engineering teams to deliver solutions.

Responsibilities:

• Advising: Agent architecture design, evaluation strategy review, and best-practice production guidance
• Building: Co-build with the customer's engineering team across the full Agent Development Lifecycle (ADLC) in outcome-scoped engagements
• Embedding: Serve as a deployed engineer inside the customer's team for extended engagements, operating as a de facto member of their org to ship agent systems directly
• Agent Engineering: ADLC end-to-end, architecture design, orchestration patterns, evals, custom conversational UIs, and production deployment
• Applied AI: Post-training, supervised fine-tuning, harness engineering, trace mining, model selection and evaluation methodology

Requirements:

• 4+ years of software engineering experience with deep expertise in Python. TypeScript/JavaScript a plus
• 2+ years of hands-on experience building and shipping production agent systems
• Strong client-facing communication skills, with the ability to confidently articulate architectural decisions to technical stakeholders (engineers, architects, CTOs)
• Strong experience with LangChain/LangGraph/Deep Agents or comparable frameworks, including multi-agent patterns and state management (short and long-term memory)
• Deep familiarity designing and implementing evaluation methodologies for non-deterministic AI systems
• Comfortable operating across the full spectrum from advisory to embedded delivery
• Willing to travel up to 20% of the time
• Exposure to dataset curation and post-training techniques (SFT, DPO, RLHF) on open-weight models using tools like Axolotl, Unsloth, Hugging Face transformers, or TRL
• Experience with trace mining to drive continuous improvement loops