Roles & Responsibilities
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 productionEngineer with versatility : APIs, orchestration pipelines, vector DBs, LLM frameworks, UI componentsOperate with agility : integrate with legacy systems, navigate ambiguity, ship safely at speedCodify patterns : build scaffolds, SDKs, and documentation to scale success across customersInfluence platform : inform product strategy through field-tested insights and extensible codeQualifications : What You Bring
Experience : 8+ years of software engineering, including 2+ years building systems in customer-facing or embedded rolesSystem architecture : Proven ability to design and implement AI-native software in production environmentsEngineering 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 managementPerformance & observability : Skilled in debugging distributed systems, tuning for latency, and implementing monitoringPlatform mindset : Can contribute to shared SDKs and tools, raising engineering velocity for the whole orgProduct sensibility : Prioritize for user value, MVP iteration, and long-term scaleDevOps fluency : Experience deploying in AWS, Azure, or GCP with CI / CD, containers, and infra-as-codeField readiness : Able to travel up to 30% to embed onsite and deliver where it mattersPreferred Qualifications
Experience integrating AI into SaaS platforms like ServiceNow or SalesforceTrack record of production deployments in secure, regulated enterprise environmentsContributions to dev experience tooling, frameworks, or reusable AI scaffoldsJoin 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.
Tell employers what skills you have
Azure
Pipelines
Customerfacing
Ships
Software Engineering
UI
Tuning
Distributed Systems
SaaS
Versatility
Angular
GCP
ServiceNow
Orchestration
Debugging
System Architecture