Forward Deployed Solution Engineer – Applied AI FDE

Description

The team sits within the company's Applied AI Forward Deployed Engineering group and works with its largest strategic customers. It finds high-value AI opportunities, helps customers get business results sooner, and creates reusable AI-native solutions for the company's platform.

Enterprises now expect AI projects to prove business value, not just potential. The work is turning advanced LLM capabilities into resilient, secure, scalable software.

As a Forward Deployed Solution Engineer (FDSE), you act as the CTO of each build, owning the work from backend services through LLM pipelines to front-end integrations. You work with customers in the field, designing and delivering solution-ready builds in agile sprints. Your software becomes the reference implementation for scalable GenAI in the enterprise, and you capture patterns and shape internal tooling. The systems you build are tested in production and designed to scale across industries.

Success means your solution builds regularly move into scaled production deployments.

Success also means writing libraries, prompts, and scaffolds that several deployments reuse.

Your work feeds into internal tooling and the platform roadmap and primitives.

You move quickly while keeping systems resilient and secure in high-stakes settings.

Architects, PMs, and customer teams trust you to lead implementation from zero to one.

Responsibilities

• Build customer-ready LLM-powered applications covering backend logic, data orchestration, and UI.
• Work alongside customers in the field to adapt, deploy, and refine solutions in live environments.
• Deliver production-ready solutions through agile end-to-end sprints.
• Use a broad toolkit: APIs, orchestration pipelines, vector databases, LLM frameworks, and UI components.
• Move with agility: connect to legacy systems, work through ambiguity, and ship safely at pace.
• Package reusable assets such as libraries, prompts, scaffolds, SDKs, and documentation to speed up future engagements.
• Send field feedback to platform and product teams to improve developer experience and inform product strategy.

Requirements

• You are a systems-minded, AI-native engineer who ships working software and can own the full stack, with equal interest in clean APIs, usable UIs, and scalable orchestration. You balance creativity with pragmatism and think like a product-minded CTO.
• You work closely with customer teams, find root causes, and design AI-powered workflows that run at scale. You debug systems, context, and customer problems, not only code.
• You have experience applying AI to work processes, decisions, or problem solving, such as using AI tools, automating workflows, analyzing AI-driven insights, or assessing AI's impact on a function or industry.
• You have 10+ years of software engineering experience, including 2+ years building systems in customer-facing or embedded roles.
• You have proven experience designing and building AI-native software that runs in production.
• You are strong in backend (Python, Node.js, Java), frontend (React, Angular), and APIs (REST, GraphQL).
• You are familiar with MLflow, Weights & Biases, LoRA, PEFT, SageMaker, Vertex AI, AzureML, Kubeflow, Airflow, Prefect, TorchServe, Triton, vLLM, SHAP, LIME, pandas, Spark, and SQL.
• You know LangChain, Semantic Kernel, prompt chaining, vector search, and context management.
• You can debug distributed systems, tune for latency, and implement monitoring.
• You can contribute to shared SDKs and tools that speed up engineering across the organization.
• You prioritize user value, iterate on MVPs, and plan for long-term scale.
• You have deployed in AWS, Azure, or GCP using CI/CD, containers, and infrastructure as code.
• You can travel up to 30% to work onsite with customers.
• Preferred: experience integrating AI into SaaS platforms such as the company's platform or Salesforce.
• Preferred: track record of production deployments in secure, regulated enterprise environments.
• Preferred: contributions to developer experience tooling, frameworks, or reusable AI scaffolds.

Conditions

Work persona (flexible, remote, or required in office) is assigned based on the nature of the role and your work location.

To confirm eligibility for a work persona, the company may measure the distance between your primary residence and the nearest company office using a third-party service.