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About This Role
### General Information
Req \#
WD00102385
Career area:
Software Engineering
Country/Region:
United States of America
State:
North Carolina
City:
Morrisville
Date:
Tuesday, July 28, 2026
Working time:
Full\-time
Additional Locations:
- United States of America \- Illinois \- Chicago
### Why Work at Lenovo
We are Lenovo. We do what we say. We own what we do. We WOW our customers.
Lenovo is a US$83 billion revenue global technology powerhouse, ranked \#196 in the Fortune Global 500, and serving millions of customers every day in 180 markets. Focused on a bold vision to deliver Smarter Technology for All, Lenovo has built on its success as the world’s largest PC company with a full\-stack portfolio of AI\-enabled, AI\-ready, and AI\-optimized devices (PCs, workstations, smartphones, tablets), infrastructure (server, storage, edge, high performance computing and software defined infrastructure), software, solutions, and services. Lenovo’s continued investment in world\-changing innovation is building a more equitable, trustworthy, and smarter future for everyone, everywhere. Lenovo is listed on the Hong Kong stock exchange under Lenovo Group Limited (HKSE: 992\) (ADR: LNVGY).
This transformation together with Lenovo’s world\-changing innovation is building a more inclusive, trustworthy, and smarter future for everyone, everywhere. To find out more visit www.lenovo.com, and read about the latest news via our StoryHub.
### Description and Requirements
About Our Team
We are building Qira, Lenovo's next\-generation cross\-device Personal AI platform that delivers intelligent, context\-aware experiences across Windows, Android, and the cloud. Our AI Enterprise Engineering team is extending Qira into an enterprise\-ready platform adopted by business customers at scale.
About the Role
The Sr. AI Enterprise Solutions Engineer is the technical bridge between Qira for Enterprise engineering and Lenovo’s commercial teams and enterprise customers. This is a hands\-on, customer\-facing individual\-contributor role for a technologist with a business mindset: someone technical enough to configure, integrate, and demo the product, and commercially fluent enough to engage CIOs and CISOs and translate what enterprises actually need back into engineering.
You will lead Lenovo’s “Customer Zero” adoption of Qira for Enterprise, run customer pilots, and be the person who has an answer in the room, not “I’ll get back to you.” This is a versatile, cross\-functional role: part solutions engineer, part customer liaison, and part project coordinator, keeping enterprise pilots and rollouts organized and moving.
Location: US Remote
What You’ll Do
- Lead “Customer Zero”: deploy, configure, and pilot Qira for Enterprise inside Lenovo, driving real adoption and surfacing requirements from live usage.
- Run enterprise pilots, proofs of concept, and demos; configure enterprise capabilities and troubleshoot hands\-on across identity, deployment, and workflow scenarios.
- Own solution design for enterprise workflow automation use cases connecting Qira to the systems employees and IT teams use every day.
- Engage CIO, CISO, and IT\-administrator stakeholders on security posture, deployment and configuration models, integration patterns, and manageability.
- Translate enterprise customer requirements into concrete engineering input, and feed ground\-level signal back to the engineering team during development.
- Support commercial alignment and go\-to\-market readiness (previews, pilots, and general\-availability milestones), partnering with sales and commercial teams.
- Provide light project and program management across enterprise pilots and rollouts: coordinate cross\-functional workstreams, track milestones, timelines, and dependencies, and keep engineering, commercial, and customer stakeholders aligned and moving.
- Build demo environments, reference configurations, and enablement materials that accelerate enterprise evaluations.
- Represent the customer’s technical reality inside engineering, and represent engineering’s capabilities credibly in front of customers.
Basic Qualifications
- 12\+ years in solutions engineering, sales/pre\-sales engineering, technical consulting, or customer\-facing enterprise software engineering.
- Hands\-on technical ability to configure, integrate, demo, and troubleshoot enterprise software, not a purely commercial or account\-management background.
- Experience engaging enterprise IT stakeholders (CIO/CISO/IT administrators) and running pilots or proofs of concept.
- Strong communication skills with the ability to move fluently between technical and business audiences.
- Bachelor’s degree in a technical field, or equivalent experience.
Preferred Qualifications
- Experience with enterprise identity and SSO (SAML, OAuth, RBAC) and/or workflow automation and iPaaS platforms such as Workato.
- Experience with AI/LLM\-based products and intelligent workflow use cases.
- Track record running B2B pilots, beta deployments, or “customer zero” / internal\-adoption programs.
- Commercial acumen, comfortable contributing to positioning, business cases, and CIO/CISO conversations.
- Project or program management experience (formal or informal): comfortable coordinating cross\-functional workstreams, timelines, and dependencies to keep initiatives on track.
- Familiarity with enterprise security, compliance, and deployment considerations (BYOC, on\-prem, data residency).
- Bachelor’s or Master’s in Engineering, Computer Science, or a related field.
The base salary range budgeted for this position is $190,000 \- $210,000\. Individuals may also be considered for bonus and/or commission.
*We are an Equal Opportunity Employer and do not discriminate against any employee or applicant for employment because of race, color, sex, age, religion, sexual orientation, gender identity, national origin, status as a veteran, and basis of disability or any federal, state, or local protected class.*
Additional Locations:
- United States of America \- Illinois \- Chicago
- United States of America
- United States of America \- Illinois
- United States of America \- Illinois \- Chicago
Salary Context
This $190K-$210K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Lenovo, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($200K) sits 7% below the category median. Disclosed range: $190K to $210K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Lenovo AI Hiring
Lenovo has 6 open AI roles right now. They're hiring across AI/ML Engineer, AI Architect, AI Engineering Manager. Positions span Morrisville, NC, US, San Jose, CA, US. Compensation range: $210K - $300K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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