Solutions Marketing Manager

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.

We’re looking for a Solutions Marketing Manager to shape how Lang Chain brings its full platform to priority industries, enterprise buyers, and high-value use cases.

This role sits at the intersection of product and GTM. You’ll translate customer problems into clear solution narratives, repeatable sales plays, campaign themes, and customer-facing assets that help teams understand where Lang Chain fits in their AI strategy.

You’ll develop industry solutions, use case solutions, and enterprise solution stories for segments such as financial services, healthcare, life sciences, and other priority markets. You’ll work closely with sales, solutions, product, demand generation, field marketing, education, and customer teams to understand what matters in the field, turn those insights into messaging and collateral, and help create programs that support pipeline and revenue generation.

The role covers the full Lang Chain platform, including Observability & Evaluation, Deployment, Sandboxes, LLM Gateway, Fleet, Engine, and OSS. The goal is to explain how these products work together to help companies build, deploy, monitor, and improve agents in production.

You’ll define the problems we solve for specific audiences, package those into repeatable narratives, and help GTM teams take them to market.

• Build solution narratives for priority industries and enterprise use cases. Initial focus areas include financial services, healthcare, and life sciences, with room to expand based on GTM priorities.

• Translate customer problems into clear solution stories. You’ll connect Lang Chain’s platform capabilities to the business, operational, governance, and technical needs of specific audiences.

• Define repeatable use case messaging. You’ll identify where agents can create value across functions and industries, then turn those patterns into solution briefs, use case pages, campaign assets, sales decks, webinar narratives, and customer-facing content.

• Build a point of view on enterprise AI adoption. You’ll help explain how companies move from prototypes and isolated experiments to production deployments with the controls, visibility, and evaluation practices they need.

• Stay close to the market. You’ll synthesize customer conversations, sales feedback, competitive activity, industry trends, and product signals into sharper messaging and GTM recommendations.

• Partner with sales, solutions, field marketing, demand generation, and customer-facing teams on targeted enterprise campaigns and account motions.

• Build sales enablement for industry and enterprise conversations. This may include talk tracks, discovery questions, objection handling, use case maps, executive-ready decks, demo narratives, and competitive context.

• Turn field learnings into…