Why This Role Matters
Enterprises are raising the bar. AI initiatives must deliver business value-not just promise potential. That means taking cutting-edge LLM capabilities and turning them into resilient, secure, and scalable software.
As a Forward Deployed Software Engineer (FDSE), you act as the CTO of the build-owning everything from backend services to LLM pipelines and front-end integrations. You partner with customers in the field to design, implement, and deliver solution-ready builds in agile sprints. Your software becomes the reference implementation for scalable GenAI in the enterprise. You codify patterns, shape internal tooling, and accelerate innovation-delivering systems that are battle-tested in production and scalable across industries.
Job Description:
Who You Are
You are a systems-minded, AI-native engineer who ships real software. You own the full stack-and are equally motivated by elegant APIs, intuitive UIs, and scalable orchestration pipelines. You think like a product-minded CTO, balancing creativity with pragmatism to deliver impact.
You embed deeply with customer teams, diagnose root problems, and architect AI-powered workflows that run at scale. You don't just debug code-you debug systems, context, and customer pain points.
You will
- Build solution-ready LLM-enabled applications that span backend logic, data orchestration, and front-end UI
- Operate in the field, working side-by-side with customers to adapt, deploy, and iterate in live environments
- Codify reusable assets-libraries, prompts, scaffolds-to accelerate future engagements
- Shape developer experience by sharing feedback with platform and product teams
What You'll Do
- Deliver end-to-end builds in agile sprints-from architecture to deployment in production
- Engineer with versatility: APIs, orchestration pipelines, vector DBs, LLM frameworks, UI components
- Operate with agility: integrate with legacy systems, navigate ambiguity, ship safely at speed
- Codify patterns: build scaffolds, SDKs, and documentation to scale success across customers
- Influence platform: inform product strategy through field-tested insights and extensible code
Qualifications:
What You Bring
- Experience: 8+ 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 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
Preferred Qualifications
- Experience integrating AI into SaaS platforms like ServiceNow or Salesforce
- Track record of production deployments in secure, regulated enterprise environments
- Contributions to dev experience tooling, frameworks, or reusable AI scaffolds
Join us at the frontier of enterprise AI-where your code powers AI transformation, your systems go live in the real world, and your ideas shape how the future scales.
