AI Engineer

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.

Today,
Lang Chain, Lang Graph, Lang Smith, and Agent Builder are used by teams shipping real AI products across startups and large enterprises. Millions of developers trust Lang Chain to power AI teams at companies like Replit, Clay, Coinbase, Workday, Lyft, Cloudflare, Harvey, Rippling, Vanta, and 35% of the Fortune 500
.

With $125M raised at Series B from IVP, Sequoia, Benchmark, Capital

G, 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.

We're looking for an AI Engineer to join our Professional Services team. You'll work directly with enterprise customers to design, build, and optimize production-grade AI agent systems. This role combines software development, AI/ML expertise, and customer-facing skills, you'll work on everything from multi-agent system design to evaluation framework implementation, requiring deep technical expertise in agent engineering with understanding of cloud infrastructure and deployment patterns.

You’ll join a collaborative team environment with a strong engineering culture, with direct impact on customer success and the opportunity to shape best practices while working with cutting-edge AI technology.

• Agent Engineering & Development: Design multi-agent systems with Subagents/Handoffs/Router patterns, implement agent logic using langchain/langgraph, design comprehensive evaluation frameworks, optimize prompts with A/B testing, implement state management (short-term and long-term memory), and design RAG patterns with vector store integration

• Deployment & Operations: Guide customers on agent deployment and configuration management, integrate agents into CI/CD pipelines, collaborate with Solution Architects on infrastructure requirements, and set up observability using Lang Smith

• Customer Engagement & Assessment: Lead agent engineering maturity assessments, work directly with enterprise customers to understand requirements and present recommendations, and partner with Solution Architects, Engagement Managers, and Product/Engineering teams

5+ years of experience in software development with 2+ years focused on AI/ML applications or agents. Customer-facing experience is preferred. We also like former founders, so if you have an unusual background, but all the right skillsets, you are welcome to apply.

Agent Engineering & Development:

• 2+ years of experience building production AI/ML applications or agents

• Strong experience with LLM frameworks (langchain/langgraph, or similar) for building agent-based applications

• Strong experience with state management (short-term and long-term memory)

• Experience designing and implementing evaluation frameworks for AI applications (LLM-as-judge, deterministic evaluators)

• Strong prompt engineering skills with experience in optimization, externalization, and A/B testing

• Experience with vector stores, RAG patterns, and knowledge organization

• Experience with MCP/tool integration, API design, and error handling patterns

• Strong Python and/or Type Script development skills with production-grade code quality

Infrastructure & Cloud:

• Understanding of cloud platforms (GCP, AWS, or Azure) and common services

• Knowledge of containerization and container orchestration concepts

• Understanding of CI/CD concepts and experience integrating applications into CI/CD pipelines

• Understanding of networking, security (authentication, authorization), and observability concepts

• Ability to collaborate with infrastructure teams on deployment requirements

Customer-Facing:

• Customer-facing…