Joblaze summary

In the role of Deployed Engineer at LangChain, the individual will engage directly with enterprise clients to design and implement production-ready AI agents, translating complex workflows into actionable software specifications. Proficiency in Python is essential, along with experience in building and deploying agent systems, while familiarity with frameworks like LangChain is a significant advantage. This position is ideal for seasoned engineers who possess strong communication skills and can navigate both advisory and hands-on roles within client teams. LangChain's growth trajectory and emphasis on impactful contributions make it a dynamic environment for professionals in the AI space.

Joblaze insights

Listed about 2 months ago — first seen on Joblaze August 28, 2026. Last confirmed on LangChain's careers page October 31, 2026.
Salary band is below the typical range for AI/ML roles (median ~$190,000).
Starts above 8% of 206 comparable senior ai/ml roles in United States that list Python we track (median $182,000 across 71 companies). See Python salary trends
Python appears in 53.1% of 556 comparable senior ai/ml roles in United States; Deep Agents appears in 0.4% of 556 comparable senior ai/ml roles in United States.

Quick facts

What's the salary range?: LangChain lists $150,000–$215,000 for this role.
How much experience is required?: At least 4 years of relevant experience for this Deployed Engineer, Professional Services (NYC) role.
What's the tech stack?: Joblaze extracted these technologies from the posting: Deep Agents, JavaScript, LangChain, LangGraph, Python, TypeScript.
What seniority level is this role?: LangChain targets senior candidates for this position.
Is this full-time or contract?: Full-time for this Deployed Engineer, Professional Services (NYC) role at LangChain.

From the original posting

About Us

About the Role

We're looking for a Deployed Engineer to join our Professional Services team, working directly with enterprise customers to build reliable, production agents. You'll translate vague enterprise workflows into concrete software specs and guide engineering teams through the resulting solution, or build it for them. You might spend a week designing a customer's agent architecture, a few weeks co-building their evaluation pipeline, or a quarter embedded inside their team shipping alongside their engineers. You are someone who's built real AI systems for production and can defend the technical tradeoffs within them.

Key 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.

Nice to Have

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

Location

New York, New York

Compensation

$150,000-$300,000 base + equity

Looking to lead technical strategy across the customer journey from Proofs of Value (PoVs) to driving overall adoption? Please check out the Deployed Engineer listing focused on Account Strategy & Solutions.

Compensation Philosophy:

Standard company text repeated across LangChain's postings is omitted here.

Deployed Engineer, Professional Services (NYC)