AI Engineer, Enablement

About Us

At Lang Chain, our mission is to make intelligent agents ubiquitous. We build the foundation for agent engineering in the real world, helping developers move from prototypes to production-ready AI agents that teams can rely on. We began as widely adopted open-source tools and have grown to also offer a platform for building, evaluating, deploying, and operating agents at scale.

With $125M raised at Series B from IVP, Sequoia, Benchmark, CapitalG, and Sapphire Ventures, we’re at a stage where we’re continuing to develop new products, growth is accelerating, and all team members have meaningful impact on what we build and how we work together. Lang Chain is a place where your contributions can shape how this technology shows up in the real world.

Today, our platform includes Lang Smith (Observability, Evaluation, Deployment, Fleet, and Sandboxes), our open source frameworks (Lang Chain, Lang Graph, and Deep Agents), and the newly launched Lang Smith Engine for autonomous agent improvement. We have 100M+ monthly open source downloads, 6,000+ active Lang Smith customers, and 5 of the Fortune 10 use Lang Smith in production (+ 35% of the Fortune 500 overall), including teams at Klarna, Clay, Coinbase, Workday, Lyft, Cloudflare, Harvey, Rippling, Vanta, Linked In, , Nvidia, and Bridgewater.

The Enablement team helps customers build real fluency with agent engineering and the Lang Smith platform through live training, hands-on workshops, and technical content that scales beyond 1:1 time.

You’ll set the technical foundation for how customers learn to build reliable agents with the Lang Chain ecosystem, teaching their teams to work effectively with Lang Chain, Lang Graph, Deep Agents, and Lang Smith through instructor-led workshops, written content, and reference implementations. We work closely with the broader GTM org to make sure every customer has the skills and confidence to build independently.

You are someone who’s built real agent systems, can defend the tradeoffs in them, and genuinely loves teaching, whether that’s a live workshop for 50 engineers or a debugging session with one stuck developer. You’ll also build the internal agents and tools that make the Enablement team itself more efficient.

• Design and deliver live, hands-on workshops that build real product fluency, not just familiarity

• Create enablement assets (tutorials, reference implementations, best-practice guides) that scale beyond individual sessions

• Offer technical guidance or office hours as questions come up

• Build internal agents and tools that streamline how the Enablement team operates, automating processes so the team scales efficiently

• Act as the voice of the customer inside Lang Chain, feeding friction points back to Product and Engineering

• Stay current on agent engineering practices and fold what you learn into what you teach

Technical:

• 3+ years building LLM/agent applications, with experience designing agent architectures and evaluation strategies

• Strong Python, comfortable writing and debugging code live, in front of a customer

Customer-facing &

Education:

• 2+ years in a technical, customer-facing role (Enablement, Customer Success Engineering, Solutions Engineering, or similar), including experience designing and delivering live workshops

• A genuine excitement for teaching, the kind where you’d rather leave a customer more capable than impressed

• Demonstrated ability to create and deliver high-quality technical training programs, including live workshops, written tutorials, documentation, and video guides

• Exceptional presentation and communication skills, with the ability to explain complex technical concepts to diverse audiences, from individual developers to enterprise stakeholders

Additional:

• Comfortable operating independently in ambiguity and managing several customer engagements at once

• Curiosity to stay at the forefront of agent engineering in industry to identify evolving trends and quickly incorporate learnings into customer enablement materials

• Willing to travel up to 20% of the time

• You’ve deployed AI agents in production, especially using Lang Chain, Lang Graph, Deep Agents, or similar frameworks

• Hands-on experience with LLM evaluation, observability, or guardrails

• Experience with cloud environments (AWS, GCP, Azure), containers, and basic Kubernetes concepts

• Type Script/JavaScript in addition to Python

Compensation:

• $150-$195k + equity

Compensation Philosophy:

We offer competitive compensation that includes base salary, variable compensation for relevant roles, meaningful equity, benefits, and perks. Actual compensation and offerings will vary based on role, level, and location. Team members in the EU, UK, and APAC receive locally competitive benefits aligned with regional norms and regulations.

Benefits include medical, dental, and vision coverage, flexible vacation, a 401(k) plan, meals on in-office days in the US and more.