AI Application Engineer

Summary

Lenovo is a global technology company delivering 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 conducting technical pre-sales, building AI proofs of concept, developing integrations, deploying solutions, enabling sales teams, and translating customer needs into reusable product models and roadmap priorities.

Responsibilities

• Pre-Sales, Discovery POC Prototyping (25% Weight)
• 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
• Full-Stack Implementation Customer Deployment (45% Weight)
• 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
• Product Model Design RD Feedback (15% Weight)
• 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
• RD Feedback Loop: Package local client-side customizations and feature requests into reusable, productized templates and advocate for their inclusion in core platform RD roadmaps
• Sales Enablement Value Engineering (15% Weight)
• 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

Qualifications

Must Haves

• 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%

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

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!