Forward Deployed Solution Engineer – Applied AI FDE

About the Position

Forward Deployed Solution Engineer – Applied AI FDE

Montreal, QUEBEC, Canada

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.

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About the Team

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.

Responsibilities

• 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.

Requirements

• AI Mastery: Demonstrated experience in using or critically thinking about integrating AI into work processes, decision-making, and problem-solving. This can include using AI-based tools, automating workflows, analyzing AI-generated insights, or evaluating the potential impact of AI on the function or industry.
• Relevant Experience: 8+ years of software engineering, including 2+ years building and deploying systems in customer-facing or embedded roles.
• Applied ML/AI Experience: 3+ years building end-to-end ML or AI systems in production: from data preparation to deployment, monitoring, and iteration. Able to reason about model selection and trade-offs between prompting, RAG, and fine-tuning - including knowing when not to use a model.
• LLM Application Development: Hands-on experience with retrieval-augmented generation (RAG), 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 evaluation harnesses for non-deterministic systems: golden datasets, offline evaluations, LLM-as-judge, A/B tests, and human-in-the-loop feedback. Able to diagnose hallucinations, quality regression, and model drift in production.
• ML Fundamentals: Practical knowledge of core ML and deep learning concepts (supervised learning, embeddings, transformers, attention, tokenization, context windows, quantization) sufficient to make sound architectural 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; as well as 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: Strong in back-end (Python, Node.js, Java), front-end (React, Angular), and API (REST/GraphQL).
• Performance and Observability: Skilled in debugging distributed systems, optimizing latency and throughput, and implementing monitoring. Specifically for AI systems: tracking tokens and costs, tracing and debugging at the span level (LangSmith, Arize, Weights & Biases, OpenTelemetry), and quality telemetry.
• MLOps and DevOps Mastery: Experience deploying on AWS, Azure, or GCP with CI/CD, containers, and infrastructure-as-code. Familiarity with model versioning, prompt/configuration versioning, automated evaluation gates in CI, and secure deployment schemes (canary, shadow, with feature flags).
• Responsible AI: Hands-on experience implementing guardrails, managing and redacting personally identifiable information (PII), mitigating prompt injections and jailbreak attempts, validating outputs, and governing data in customer environments.
• Platform Mindset: Ability to contribute to shared SDKs and tools, increasing engineering velocity for the entire organization.
• Product Sensibility: Prioritizes user value, MVP iteration, and long-term scale.
• Field Availability: Able to travel up to 30% of the time to embed on-site and deliver where it matters.

Preferred Qualifications

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We Offer

• Competitive salary
• Opportunity to work with cutting-edge AI technologies
• Collaborative and innovative work environment
• Professional development opportunities
• Health and wellness benefits

About the Company

ServiceNow is a global leader in delivering workplace technology that helps people work better and companies operate more efficiently. Our platform connects people, systems, and processes to enable organizations to find smarter, faster, and better ways of working. Join us in our mission to make the world work better for everyone.