AI Application Engineer

Lenovo•Morrisville, NC

1d•$110,600 - $169,510•Remote

About The Position

The AI Application Engineer is a highly versatile, customer-facing technical champion responsible for the end-to-end commercialization and deployment of Lenovo’s Customer Service AI Solution (CSAS) offering. Bridging the gap between software engineering, pre-sales consulting, and product design, this role acts as the primary technical partner for clients. You will lead technical pre-sales discussions, build functional AI proofs-of-concept (POCs), deploy core solution integrations, conduct internal sales enablement training, and translate real-world client requirements into reusable product models and roadmap priorities for our core R&D teams.

Requirements

Bachelors Degree in Computer Science, Engineering or related field
Engineering Practice: 3–5+ years of software engineering, solution engineering, or systems integration experience in a customer-facing or consultative capacity.
AI Application Experience: Hands-on experience building, testing, or deploying applications utilizing LLM APIs, vector databases, and GenAI orchestration frameworks (e.g., LangChain, LlamaIndex, OpenAI/Claude APIs).
Full-Stack Tooling: Proficient in Python (FastAPI, Flask) or JavaScript/TypeScript (Node.js, React) and building REST/GraphQL API integrations.
Infrastructure Knowledge: Experience working with cloud platforms (AWS, Azure, or GCP) and containerization tools (Docker).

Nice To Haves

Enterprise SaaS Platforms: Strong experience integrating with major enterprise developer platforms (e.g., Salesforce, ServiceNow, HubSpot, or Veeva).
Product Mindset: Demonstrated experience capturing ambiguous requirements and documenting them as structured technical requirements or solution architectures.
Grit & Problem-Solving: Ability to debug complex data-flow, networking, and rate-limiting issues under tight timelines in live client environments.

Responsibilities

Technical Pre-Sales: Partner with business development and sales teams to pitch Lenovo’s AI offerings, demonstrating technical feasibility and handling deep architectural discovery with client IT teams.
Rapid POC Development: Design and build functional, high-impact AI Proof-of-Concepts (POCs) demonstrating RAG (Retrieval-Augmented Generation) capabilities, model accuracy, and system integration.
Custom Integration Development: Write production-grade, fault-tolerant Python/TypeScript connectors, data transformers, and workflow scripts to link Lenovo’s AI platform with legacy customer CRM, IVR, and database systems (e.g., Salesforce, ServiceNow, on-prem databases) with a "Zero Rip-and-Replace" philosophy.
On-Site Ingestion & Operations: Lead the deployment of containerized (Docker, Kubernetes) AI solutions within client-managed private or hybrid cloud environments.
Field-Driven Product Design: Act as a technical Product Manager in the field, clarifying ambiguous customer requirements, creating structured solution designs, and defining repeatable product models.
R&D Feedback Loop: Package local client-side customizations and feature requests into reusable, productized templates and advocate for their inclusion in core platform R&D roadmaps.
Internal Sales Enablement: Develop and deliver technical training to internal sales, delivery, and marketing teams to build organization-wide capability in pitching and positioning our AI offerings.
Value Instrumentation: Design and implement telemetry (using structured logs, open-source tracing, or Prometheus) to track, measure, and mathematically prove business-level KPIs (e.g., Average Handling Time reductions, first-contact deflection, and ROI) directly to client stakeholders.