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About This Role
### General Information
Req \#
WD00101530
Country/Region:
United States of America
State:
North Carolina
City:
Morrisville
Date:
Monday, July 27, 2026
Working time:
Full\-time
Additional Locations:
- United States of America \- North Carolina \- Morrisville
### 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
Role Overview:
The Forward Deployed Engineer (FDE) is a hands\-on AI engineer and transformation practitioner embedded within Marketing and Sales functions to accelerate Lenovo’s Solutions and Services Group's (SSG) AI\-led Marketing \& Sales transformation agenda.
This role partners with domain leaders to identify, build, and scale AI solutions across Market\-to\-Opportunity (M2O) and Quote\-to\-Cash (Q2C) processes — delivering measurable outcomes such as revenue growth, sales productivity, pricing optimization, and cycle time reduction.
The FDE operates in a sprint\-based, execution\-focused model, owning delivery from problem discovery to deployed solution and adoption, aligning with enterprise transformation priorities, governance, and value realization.
Key Responsibilities:
1\. Business Discovery \& Use Case Definition
- Embed within Marketing \& Sales teams to assess end\-to\-end workflows across lead generation, pipeline management, pricing, quoting, and order fulfillment
- Identify high\-value AI opportunities aligned to commercial KPIs and transformation priorities
- Translate business problems into scoped, executable AI use cases
2\. AI Solution Build \& Deployment
- Design and deliver AI solutions leveraging:
- + Generative AI, Agentic AI, predictive analytics
+ Microsoft AI ecosystem (Copilot Studio, Azure OpenAI, M365, Power Platform)
+ RAG pipelines, agent orchestration, and automation tools
- Rapidly prototype, test, and iterate within sprint cycles
- Deliver production\-grade solutions integrated into existing enterprise systems (CRM, CPQ, ERP)
3\. Commercial Transformation Impact
- Drive improvements across:
- + Revenue velocity and pipeline conversion
+ Quote\-to\-cash cycle time and deal execution
+ Pricing optimization and sales effectiveness
- Enable digital transformation across sales, pricing, and revenue operations processes
4\. Adoption \& Change Enablement
- Ensure solutions are adopted, embedded, and scaled within business workflows
- Partner with business stakeholders to drive change management and user adoption
- Capture feedback and continuously enhance delivered solutions
5\. Transformation Alignment \& Governance
- Align delivery with SSG Digital Transformation roadmap, KPIs, and ROI targets
- Contribute to performance tracking, dashboards, and transformation scorecards
- Provide regular updates into program governance cadence (weekly reviews, SteerCo inputs)
6\. Stakeholder Collaboration
- Act as a bridge between business stakeholders, IT, data platforms, and AI teams
- Communicate progress, risks, and trade\-offs clearly across technical and non\-technical audiences
- Support cross\-functional alignment in complex, global environments
Qualifications:
- 5\+ years of experience in software engineering, applied AI, or solutions engineering
- Proven experience delivering AI/automation solutions in business environments
- Strong knowledge of LLMs, prompt engineering, RAG, agent\-based systems
- Integration experience with enterprise platforms (CRM, ERP, CPQ, APIs)
- Exposure to Marketing, Sales, or Revenue Operations domains
- Demonstrated ability to deliver measurable business outcomes and drive adoption
- Strong communication skills across business and technical stakeholders
- Experience with Microsoft AI stack (Copilot Studio, Azure OpenAI, Power Platform)
- Background in commercial transformation, pricing, or sales enablement
- Experience in consulting, solutions architecture, or cross\-functional delivery models
- Understanding of transformation governance, KPIs, and ROI tracking frameworks
Basic Requirements:* 5\+ years of experience in software engineering, applied AI, or solutions engineering
\#LI\-MM5
\#LI\-Hybrid
*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 \- North Carolina \- Morrisville
- United States of America
- United States of America \- North Carolina
- United States of America \- North Carolina \- Morrisville
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. Mid-level AI roles across all categories have a median of $194,400.
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.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
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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