Forward Deployed Solution Engineer – Applied AI FDE

Team Bio: ServiceNow’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 Senior 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. 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 Production - ready solution in agile end-to-end sprints - 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 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 - Engineering leadership: You are trusted by architects, PMs, and customer teams to lead implementation from zero to one - AI fluency — Demonstrated experience leveraging or critically thinking about how to integrate AI into work processes, decision-making, and problem-solving. This may include using AI-powered tools, automating workflows, analyzing AI-driven insights, or assessing AI's potential impact on the function or industry. - Relevant experience — 8+ years of software engineering, including 2+ years building and shipping systems in customer-facing or embedded roles. - Applied ML/AI experience — 3+ years building production ML or AI systems end to end: data preparation through deployment, monitoring, and iteration. Can reason about model selection and the tradeoffs between prompting, RAG, and fine-tuning — including when not to use a model. - LLM application development — Hands-on experience with retrieval-augmented generation, embeddings and vector databases (pgvector, Pinecone, Weaviate, FAISS), prompt engineering and chaining, structured outputs and function/tool calling, context management, and agentic or multi-step workflows. - Evaluation rigor — Ability to define success metrics and build eval harnesses for non-deterministic systems: golden datasets, offline evals, LLM-as-judge, A/B testing, and human-in-the-loop feedback. Can diagnose hallucination, quality regression, and model drift in production. - ML foundations — Working knowledge of core ML and deep learning concepts (supervised learning, embeddings, transformers, attention, tokenization, context windows, quantization) sufficient to make sound architecture decisions and collaborate credibly with data science partners. - Frameworks and platforms — LangChain/LangGraph, LlamaIndex, Semantic Kernel, or equivalent; PyTorch, TensorFlow, or scikit-learn; the Hugging Face ecosystem; and major model APIs and platforms (Anthropic, OpenAI, Amazon Bedrock, Azure AI Foundry, Vertex AI). - 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), and APIs (REST/GraphQL). - Performance & observability — Skilled in debugging distributed systems, tuning for latency and throughput, and implementing monitoring. For AI systems specifically: token and cost tracking, tracing and span-level debugging (LangSmith, Arize, Weights & Biases, OpenTelemetry), and quality telemetry. - MLOps & DevOps fluency — Experience deploying in AWS, Azure, or GCP with CI/CD, containers, and infrastructure-as-code. Familiarity with model versioning, prompt/config versioning, automated eval gates in CI, and safe rollout patterns (canary, shadow, feature-flagged). - Responsible AI — Practical experience implementing guardrails, PII handling and redaction, prompt injection and jailbreak mitigation, output validation, and data governance in customer environments. - Platform mindset — Can contribute to shared SDKs and tools, raising engineering velocity for the whole org. - Product sensibility — Prioritizes for user value, MVP iteration, and long-term scale. - 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. - Fine-tuning and adaptation techniques (LoRA/PEFT, distillation, quantization) and knowing when they beat prompting or retrieval. - Inference optimization and self-hosted serving (vLLM, TensorRT-LLM, Triton), including GPU cost/performance tuning. - Data engineering for ML — pipelines, feature stores, streaming ingestion, and document processing at scale. - Experience delivering AI systems in regulated or data-sensitive environments (financial services, healthcare, public sector). - Comfort leading technical workshops, discovery sessions, and architecture reviews with customer stakeholders.