Forward Deployed Solution Engineer (Applied AI) in London

• 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
• 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
• 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

• Platform mindset: Can contribute to shared SDKs and tools, raising engineering velocity for the whole orgLLM tooling: Familiarity with LangChain, Semantic Kernel, prompt chaining, vector search, and context managementPerformance & observability: Skilled in debugging distributed systems, tuning for latency, and implementing monitoringProduct sensibility: Prioritize for user value, MVP iteration, and long-term scaleField 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 rolesEngineering depth: Strength in backend (Python, Node.js, Java), frontend (React, Angular), APIs (REST/GraphQL)DevOps fluency: Experience deploying in AWS, Azure, or GCP with CI/CD, containers, and infra-as-codeSystem architecture: Proven ability to design and implement AI-native software in production environmentsExperience: 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

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