Interested in this AI/ML Engineer role at LightEdge?
Apply Now →About This Role
Lightedge is hiring a Subject Matter Expert (SME) to join the Strategic Solutions Group (SSG), our dedicated overlay function supporting the company's largest and most complex opportunities. This SME will bring deep, cross\-domain fluency in AI infrastructure, Broadcom VCF/private cloud, IBM Power, and security to every stage of the strategic sales cycle — from opportunity qualification through technical validation, proposal, and close. Beyond deal support, this person will represent Lightedge externally: engaging our strategic technology alliances (Broadcom, IBM, and others) and presenting at industry conferences and partner events as a credible, technical voice for the company.
### Responsibilities:
- Support strategic opportunities ($30K\+ MRR or strategic use cases) end\-to\-end — from discovery and qualification through solution design, proposal, technical validation, and close — in partnership with the assigned Account Executive and Solutions Architect.
- Serve as the technical and business authority across four domains: AI infrastructure (GPUaaS, AI Factory, data\-sovereign compute), Broadcom VCF / private cloud, IBM Power managed cloud, and security \& compliance.
- Translate complex, multi\-domain technical requirements into a clear, defensible solution narrative that sales and customer stakeholders can act on.
- Partner with the SAE and SSG leader (Dan Kurtz) on deal strategy, MEDPICC qualification, and forecast integrity for every assigned strategic opportunity.
- Lead technical discovery sessions, whiteboard sessions, and executive\-level solution presentations directly with prospective and existing customers.
- Own the required opportunity artifacts and rules of engagement defined for the SSG sales process, ensuring consistency across every strategic deal.
- Engage directly with strategic partner counterparts at Broadcom and IBM to align joint account strategy, co\-sell motions, and technical enablement.
- Represent Lightedge at industry conferences, partner events, and customer forums as a subject matter expert and public\-facing technical spokesperson.
- Contribute field intelligence and win/loss insight back into the SSG proof\-of\-concept tracking (win\-rate impact on $30K\+ deals) supporting the 2027 dedicated enterprise team business case.
- Provide enablement and coaching support to sellers and Solutions Architects to raise the organization's fluency across AI, Broadcom, IBM, and security.
### Experience:
- 15\+ years in a pre\-sales, solutions engineering, or technical sales role supporting complex, multi\-product enterprise deals.
- Demonstrated expertise in at least three of the four core domains, with working knowledge of the fourth: AI infrastructure/GPUaaS, Broadcom VCF/VMware private cloud, IBM Power (IBM i, AIX, or Power Linux), and security/compliance frameworks.
- Track record of engaging directly with strategic technology partners (e.g., Broadcom, IBM, hyperscalers) on joint go\-to\-market and co\-sell motions.
- Comfort and credibility presenting to executive audiences, at partner events, and on public conference stages.
- Strong opportunity qualification discipline (MEDPICC, BANDT, or equivalent) and the ability to co\-own forecast accuracy on strategic deals.
- Excellent written and verbal communication skills; able to move fluidly between deep technical detail and executive business narrative.
- Willingness to travel for customer engagements, partner events, and industry conferences.
- Relevant certifications a plus: Broadcom/VMware (VCF, VCP), IBM Power/IBM i or AIX credentials, and security certifications (e.g., CISSP, Security\+).
### Why this role matters:
This SME role sits inside the newly formed Strategic Solutions Group, launched to bring dedicated ownership, technical depth, and process discipline to Lightedge's largest opportunities. Your work directly supports the company's double\-digit growth targets.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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 LightEdge, 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 in Demand for This Role
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. C-Level-level AI roles across all categories have a median of $250,000.
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.
LightEdge AI Hiring
LightEdge has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Kansas City, MO, US.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.