We’re seeking an exceptional Forward Deployed Engineer to build and deploy production LLM systems with enterprise customers. You’ll turn model capabilities into reliable software operating inside real organizations. The role sits at the intersection of product engineering, applied research, and customer delivery, shipping systems under compute, latency, and governance constraints while feeding field insights back into the platform and model roadmap. Systems developed in the field will later become core product capabilities across customers.
The Opportunity
As a Forward Deployed Engineer on our team, you’ll own the end-to-end lifecycle of enterprise model deployments. You will architect, build, and operate production systems while coordinating across customer engineering, security, IT, data, and business stakeholders as well as internal product and research teams.
Your work will span:
- Multi-Workstream Technical Delivery: Work with customer engineering teams to translate business problems into production LLM systems, coordinating model development, tool integrations, evaluation systems, and deployment infrastructure.
- LLM Application Architecture: Design and ship advanced LLM systems including agent architectures, tool use pipelines, structured output workflows, caching layers, and fallback mechanisms
- Production System Development: Implement end-to-end solutions including APIs, orchestration layers, tool connectors, retrieval systems, and user-facing integrations, while maintaining deployment infrastructure such as CI/CD pipelines, observability dashboards, and operational runbooks
- Evaluation, Safety & Reliability: Build bespoke evaluation harnesses and monitoring infrastructure including offline evaluation sets, regression gates, online quality metrics, and guardrail systems to ensure safe and reliable production behavior.
- Enterprise Data Integration: Design secure integrations with enterprise data systems and infrastructure, implementing authentication, authorization, auditability, and data-boundary controls while collaborating with security and infrastructure teams to meet enterprise requirements
- Adoption & Field Strategy: Work closely with customer teams to drive production adoption through rollout planning, training, and change management, while synthesizing field insights into product and research feedback that shapes the technological development roadmap.
What You'll Bring
- BS, MS, or PhD in Computer Science, Electrical Engineering, Robotics or equivalent practical experience
- Strong programming ability with production experience in Python, with experience in systems languages such as C++, Rust, or C.
- Experience designing and implementing evaluation frameworks, monitoring systems, and reliability controls for AI systems
- Ability to design systems end-to-end, spanning APIs, orchestration layers, reliability, latency constraints, and enterprise integrations
- Experience building secure integrations with enterprise infrastructure including authentication, authorization, auditability, and data-boundary controls
- Strong execution and ownership mindset; comfortable operating in ambiguous environments where requirements evolve rapidly.
Ideal Candidates Will Also Have
- Experience building reusable internal platforms, deployment tooling, or engineering playbooks that accelerate future deployments
- Experience operating AI systems in production environments with strict latency, compute, reliability, and governance constraints