Forward Deployed Solution Engineer

Description

Team Bio:

The company’s Applied AI Forward Deployed Engineering (FDE) team is where bold ideas meet transformative action. We partner with our most strategic customers to shape the future of enterprise AI. Together, we identify high-value opportunities, accelerate business outcomes, and build reusable AI-native solutions that advance the Now AI Platform.

Our mission: We partner deeply with our customers to build intelligent, scalable AI solutions that solve their most mission-critical challenges. By embedding in real-world complexity, we deliver fast, iterate with purpose, and transform every success into reusable patterns that accelerate transformation across the Now Platform and the broader enterprise.

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.

Responsibilities

• As a GenAI/ML Engineer on the FDE team, you will:
• Forward Deployed Engineers sit alongside our customers and turn ambiguous business problems into working AI systems.
• Partner directly with customers to discover problems and frame high-impact AI use cases.
• Design and build end-to-end GenAI/ML systems — RAG pipelines, agentic workflows, evaluation, and guardrails — and ship them to production.
• Prototype quickly, iterate with real users, and harden pilots into reliable, monitored services.
• Own the technical relationship, translating between business stakeholders and engineering.
• Raise the bar for ML depth and engineering quality across the team.
• What Success Looks Like:
• Production-grade delivery: Your solution builds consistently convert to scaled deployments in production 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

Requirements

• 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.
• What You Bring:
• 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: 8+ years of software engineering, including 2+ years building systems in customer-facing or embedded roles. Experience building machine learning/ GenAI systems with modern AI/ML technologies, including LLMs, embeddings, vector databases, and RAG architectures.
• 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 the company or Salesforce
• Track record of production deployments in secure, regulated enterprise environments
• Contributions to dev experience tooling, frameworks, or reusable AI scaffolds
• Additional Information
• Work Personas
• We approach our distributed world of work with flexibility and trust. Work personas (flexible, remote, or required in office) are categories that are assigned to the company employees depending on the nature of their work and their assigned work location. Learn more here . To determine eligibility for a work persona, the company may confirm the distance between your primary residence and the closest the company office using a third-party service.