Note: The job is a remote job and is open to candidates in USA. Lenovo is a global technology company focused on AI-enabled devices, infrastructure, software, solutions, and services. The AI Application Engineer will lead the commercialization and deployment of Lenovo’s Customer Service AI Solution by supporting technical pre-sales, developing AI proofs of concept, implementing customer integrations, and translating client needs into product models and R&D priorities.
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
Skills
- 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)
- This role offers the flexibility to be home-based anywhere in the United States
- This role will require travel which may be up to 25%
- 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
Benefits
- Individuals may also be considered for bonuses and/or commissions.
- This role offers the flexibility to be home-based anywhere in the United States.
- If you're near our Chicago or Raleigh offices, we follow a friendly hybrid model with three days a week in the office - great for collaboration and connection!
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