Job detail for Forward Deployed Solution Engineer

Company Description

It all started under the San Diego sun in 2004, when a visionary engineer, Fred Luddy, saw the potential to transform the way we work. Today, ServiceNow stands as a global market leader, bringing innovative, AI-enhanced technology to over 8,100 customers, including 85% of the Fortune 500®. Our intelligent, cloud-based platform seamlessly connects people, systems, and processes to enable organizations to find smarter, faster, and better ways to work. But this is just the beginning of our journey. Join us in our pursuit of our goal to make the world work better for everyone.

It all started when engineer Fred Luddy wrote code that automated a tedious task for his coworker, Phyllis. She cried tears of joy. That moment inspired Fred to build a company that could do that for everyone—freeing people from busywork so they could focus on meaningful work. Today, ServiceNow is the AI control tower for business reinvention. Our ServiceNow AI platform brings together any AI, any data, and any workflow— helping 85% of the Fortune 500® work smarter, faster, and better. We're building an AI-native culture where technology and talent are unstoppable together. And we're just getting started.

Join us to put AI to work for people.

Job Description

Team Overview

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

Forward Deployed Engineers sit alongside our customers and turn ambiguous business problems into working AI systems. As a GenAI/ML Engineer on the FDE team, you will:

• 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.

Who You Are:

Builders first. We do not weigh pedigree — we want customer-obsessed engineers with genuine ML depth and the engineering breadth to ship end to end, who move fast from idea to working software.

You are a dynamic and innovative problem-solver who thrives in complex, fast-paced environments. With a strong analytical mindset and a passion for AI, you excel at transforming ambiguity into clarity. Your ability to synthesize data, workflows, and user motivations allows you to identify impactful solutions that align with customer needs and business objectives.

You embody ServiceNow’s values:

• Customer First: You prioritize delivering value through business outcomes.
• Bold Innovation: You challenge convention and seek simplicity through design.
• One Team: You collaborate deeply across functions and build through shared ownership.
• Integrity and Belonging: You build trust, foster inclusion, and lead with empathy.

What You’ll Do:

• Lead Strategic Discovery: Identify high-impact AI opportunities by running hands-on workshops and aligning stakeholders.
• Architect AI-Native Solutions: Design systems using LLMs, RAG pipelines, retrieval logic, and workflow orchestration.
• Accelerate Delivery with Engineers: Collaborate closely with FDSEs to build, test, and iterate functional solutions quickly.
• Codify Best Practices: Create repeatable frameworks, reusable templates, and modular components.
• Drive Alignment: Influence customer and executive buy-in through clear storytelling and solution framing.
• Advocate Field Insights: Capture feedback from deployments to shape product strategy and prioritize platform needs.
• Enable Scale: Equip internal teams and customers to expand success through documentation and technical onboarding assets.

What Success Looks Like:

• Solution-Ready Build Delivered: You deliver a validated AI solution-ready build within 8–12 weeks that directly addresses a business-critical problem.
• Widespread Reuse: Your assets—from prompts to orchestration logic—are leveraged by other teams and codified for scale.
• Platform Evolution: Your work contributes to shaping the Now Platform through structured product feedback and architectural influence.
• Business Transformation: The AI solution you’ve led results in measurable gains in efficiency, automation, adoption, or satisfaction.
• Production Trajectory: Your delivered solution becomes the foundation for scaled production deployment across customer environments.
• Trusted Field Partner: Customers and internal stakeholders see you as a strategic, dependable, and technically credible partner.

Qualifications

• 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.

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 ServiceNow employees depending on the nature of their work and their assigned work location. Learn more here. To determine eligibility for a work persona, ServiceNow may confirm the distance between your primary residence and the closest ServiceNow office using a third-party service.

Equal Opportunity Employer

ServiceNow is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, national origin, age, disability, gender identity, veteran status, or any other category protected by law. In addition, all qualified applicants with arrest or conviction records will be considered for employment in accordance with legal requirements.

Accommodations

We strive to create an accessible and inclusive experience for all candidates. If you require a reasonable accommodation to complete any part of the application process, or are unable to use this online application and need an alternative method to apply, please contact globaltalentss@servicenow.com for assistance.

Export Control Regulations

For positions requiring access to controlled technology subject to export control regulations, including the U.S. Export Administration Regulations (EAR), ServiceNow may be required to obtain export control approval from government authorities for certain individuals. All employment is contingent upon ServiceNow obtaining any export license or other approval that may be required by relevant export control authorities.

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