Forward Deployed Solutions Engineer (Applied AI)

ServiceNow; Dublin; Híbrido; EUR 120.000 - 180.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Generous family leave

Matched donations

Annual learning stipends

Flexible PTO

Competitive retirement plan

Paid volunteer time

Descripción de la vacante

ServiceNow’s Applied AI Forward Deployed Engineering (FDSE) team partners with strategic customers to design, implement, and deliver solution-ready builds in agile sprints. You own backend services to LLM pipelines and front-end integrations, producing reference implementations for scalable GenAI in the enterprise.

Ideal candidates craft reusable patterns, contribute to internal tooling, and drive AI-native software that runs in production across industries, with up to 30% travel to embed onsite.

Formación

• 10+ years of software engineering experience, including 2+ years building systems in customer-facing or embedded roles.
• Experience deploying in AWS, Azure, or GCP with CI/CD, containers, and infra-as-code.
• Strong backend (Python, Node.js, Java) and frontend (React, Angular) knowledge, plus APIs (REST/GraphQL).
• Experience integrating AI into SaaS platforms like ServiceNow or Salesforce.

Responsabilidades

• Build solution-ready LLM-enabled applications spanning backend logic, data orchestration, and front-end UI.
• Operate in the field, collaborating with customers in live environments to adapt and iterate.
• Codify reusable assets—libraries, prompts, scaffolds—for future engagements.
• Shape developer experience by feeding feedback to platform and product teams.
• Deliver production-ready solutions in agile end-to-end sprints.
• Engineer with APIs, orchestration pipelines, LLM frameworks, and UI components.

Conocimientos

Python

Node.js

Java

React

Angular

REST APIs

GraphQL

DevOps

CI/CD

AWS

Azure

GCP

MLflow

SageMaker

LangChain

Vector search

Herramientas

LangChain

Semantic Kernel

Prompt chaining

Vector DBs

Descripción del empleo

• 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
• 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 Solution 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
• 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
• 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

Benefits

• Generous family leave
• Matched donations
• Annual learning stipends
• Flexible PTO
• Competitive retirement plan
• Paid volunteer time

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 impactYou 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 pointsDevOps fluency: Experience deploying in AWS, Azure, or GCP with CI/CD, containers, and infra-as-codePlatform mindset: Can contribute to shared SDKs and tools, raising engineering velocity for the whole orgField readiness: Able to travel up to 30% to embed onsite and deliver where it mattersMachine Learning: Foundations of MLflow, Weights & Biases, LoRA, PEFT, SageMaker, Vertex AI, AzureML, Kubeflow, Airflow, Prefect, TorchServe, Triton, vLLM, SHAP, LIME, pandas, Spark, SQLRelevant Experience: 10+ years of software engineering, including 2+ years building systems in customer-facing or embedded rolesPerformance & observability: Skilled in debugging distributed systems, tuning for latency, and implementing monitoringEngineering 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 managementSystem architecture: Proven ability to design and implement AI-native software in production environmentsProduct sensibility: Prioritize for user value, MVP iteration, and long-term scaleExperience: 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 industryContributions to dev experience tooling, frameworks, or reusable AI scaffoldsTrack record of production deployments in secure, regulated enterprise environmentsExperience integrating AI into SaaS platforms like ServiceNow or Salesforce