
Director, Forward Deployed Solutions Engineer – Applied AI FDE
- Santa Clara, CA
- Permanent
- Full-time
- Lead Teams Building Solution-Ready Applications: Guide engineering teams in developing LLM-enabled applications that span backend logic, data orchestration, and front-end UI—ensuring technical excellence and business alignment.
- Drive Field Execution: Oversee customer-embedded engagements, enabling your teams to adapt, deploy, and iterate solutions in live environments with agility and precision.
- Codify Reusable Engineering Assets: Direct the creation of reusable libraries, prompt scaffolds, and modular components that accelerate future delivery and scale impact.
- Shape Developer Experience: Channel field insights into platform and product teams, influencing roadmap priorities and improving tooling for broader engineering adoption.
- Deliver End-to-End Solutions Through Teams: Ensure your teams deliver complete builds—from architecture to production-ready deployment—within agile sprint cycles.
- Guide Versatile Engineering Practices: Mentor engineers across the stack—APIs, orchestration pipelines, vector databases, LLM frameworks, and UI components—fostering technical depth and adaptability.
- Enable Agile Execution: Help teams navigate legacy integrations, ambiguity, and high-stakes environments while maintaining speed and safety in delivery.
- Scale Through Codified Patterns: Lead the development of scaffolds, SDKs, and documentation that enable repeatable success across customer engagements.
- Influence Platform Strategy: Translate field-tested insights into actionable feedback for product and platform teams, driving extensible improvements and innovation.
- Production-ready delivery: Your solution builds consistently convert to scaled deployments in production-ready environments
- Reusable impact: You author libraries, prompts, and scaffolds that power multiple deployments and projects
- Platform influence: Your work shapes internal tooling and is integrated into platform roadmap and primitives
- Velocity and precision: You move fast without breaking things—shaping resilient, secure systems in high-stakes contexts
- Engineering leadership: You are trusted by architects, PMs, and customer teams to lead implementation from zero to one
- Experience: In leveraging or critically thinking about how to integrate AI into work processes, decision-making, or problem-solving. This may include using AI-powered tools, automating workflows, analyzing AI-driven insights, or exploring AI’s potential impact on the function or industry.
- Relevant Experience: 13+ years of software engineering, including 2+ years building systems in customer-facing or embedded roles
- System architecture: Proven ability to design and implement AI-native software in production-ready environments
- Engineering depth: Strength in backend (Python, Node.js, Java), frontend (React, Angular), APIs (REST/GraphQL)
- LLM tooling: Familiarity with LangChain, Semantic Kernel, prompt chaining, vector search, and context management
- Performance & observability: Skilled in debugging distributed systems, tuning for latency, and implementing monitoring
- Platform mindset: Can contribute to shared SDKs and tools, raising engineering velocity for the whole org
- Product sensibility: Prioritize for user value, MVP iteration, and long-term scale
- DevOps fluency: Experience deploying in AWS, Azure, or GCP with CI/CD, containers, and infra-as-code
- Field readiness: Able to travel up to 30% to embed onsite and deliver where it matters
- Experience integrating AI into SaaS platforms like ServiceNow or Salesforce
- Track record of production-ready deployments in secure, regulated enterprise environments
- Contributions to dev experience tooling, frameworks, or reusable AI scaffolds