Interested in this AI/ML Engineer role at Lenovo?
Apply Now →Skills & Technologies
About This Role
### General Information
Req \#
WD00099384
Career area:
Artificial Intelligence
Country/Region:
United States of America
State:
North Carolina
City:
Morrisville
Date:
Tuesday, June 9, 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
We are seeking a talented and experienced Senior AI Developer to design, implement, and scale out an Agentic AI platform built on Intel CPU/GPU/NPU and edge infrastructure. This role focuses on building a foundation platform that enables both developers and non\-technical users to create, orchestrate and deploy AI agents using Intel AI super builder, MCP\-based workflows and multimodal AI pipelines. You will work across the full lifecycle of AI systems – from agent creation and orchestration to multimodal processing and business outcome optimization – ensuring solutions are scalable, reliable and production \-ready across edge and cloud environments.
This role sits within Lenovo’s Solutions \& Services Group (SSG), the global organization that brings together our end‑to‑end AI solutions and services to turn customer vision into value. You'll be joining a new distributed engineering team which extends Personal agentic platforms like OpenCLAW, Hermes etc. into AI native personal delivery platforms that powers Lenovo's Agentic AI strategy across Hybrid cloud, on\-Prem and edge.
Key Responsibilities:
Platform and Architecture
- Design and develop a scalable Agentic AI platform leveraging Intel AI super builder and MCP based architectures
- Build end to end systems including Agent frameworks, orchestration layers, inference pipelines and user interfaces
- Integrate AI solutions across edge, on\-premise and cloud environments
Agent Frameworks and workflow orchestration
- Develop and Extend MCP\-based work flow orchestration and Data ingestion systems
- Implement Trigger\-based workflows ( Scheduled RAG \+ MCP pipelines)
- Multi\-agent Coordination pipelines ( eg : evaluate à summarize à score)
- Seamless integration of multiple data source and MCP servers
- Build reusable agent templates and tools
Multimodal AI systems
- Develop multi modal AI pipelines for
- + Audio ( transcription, summarization, chapterization)
+ Video ( CCTV/Ring\-style summarization, event detection)
+ Vision\-language reasoning (VLM\-based systems)
- Implement orchestration and validation pipelines for multimodal workflows
- Optimize inference for edge deployment on Intel CPU/GPU/NPU platforms
Business\-oriented Agents
- Build domain\-specific AI agents aligned to measurable business outcomes, including
- + Staff interaction tracking
+ Customer journey intelligence
- Translate AI outputs into business KPIs and actionable insights
- Collaborate with stake holders to align AI systems with revenue, efficiency and customer experience goals
Control plane, GenAIOps and performance optimization
- Design and implement a centralized control plane for Agent orchestration, routing scheduling and life cycle management
- Design and reliability agent for monitoring, evaluation, guardrails
- Maximize utilization across Intel CPU/GPU/NPU and dynamically schedule workloads
Basic Qualifications:
- Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, or a related field.
- 5\+ years in AI/ML system development across domains (NLP, CV, Multimodal AI)
Preferred Qualifications:
- 2\+ years of experience in developing Generative AI / Agentic AI systems in production environments
- Strong experience with
- + LLMs, SLMS, VLMS
+ Agent orchestration frameworks ( MCP or similar paradigms)
- Experience building multi\-agent systems and workflow pipelines
- Knowledge of RAG architectures and vector databases
Systems and Infrastructure
- Experience with Edge \+ on prem \+ cloud deployments
- Kubernetes and containerized environments
- Familiarity with Intel AI stack ( Open Vino, CPU/GPU optimization) is a plus
- Experience with NVIDIA stack ( CUDA, TensorRT) is a plus
Programming
- Strong coding skills in Python ( Mandatory)
- Frontend frameworks ( React or similar)
- Scripting and automation tools
What Makes this role Unique
- Opportunity to build production \-grade Agentic AI platform on Intel Hardware
- Work across
- + Agent orchestration ( MCP)
+ Multimodal AI systems
+ Edge\-to\-cloud deployment architectures
- Direct impact on business KPI\-driven applications in real \-world environments
This role offers the flexibility to be home\-based anywhere in the U.S. with preference for the Eastern or Central time zones. 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!
The base salary range budgeted for this position is $150,000 to $175,000 Individuals may also be considered for bonuses and/or commissions. Lenovo’s various benefits can be found at \[1] www.lenovobenefits.com
In compliance with Colorado's EPEWA, the expected Application Deadline for this position is 8/1/2026 \- this applies to both internal and external candidate
If you are passionate about AI development and eager to take on a challenging yet rewarding role that makes a real impact, we invite you to connect with us!
*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
Salary Context
This $150K-$175K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($162K) sits 10% below the category median. Disclosed range: $150K to $175K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Lenovo AI Hiring
Lenovo has 6 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer. Based in Morrisville, NC, US. Compensation range: $170K - $233K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.