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Who We Are:
Delta Electronics is a global innovator at the forefront of technologies powering the future. Since 1971, we’ve been leading the way in high\-efficiency power and thermal management solutions. Today, we’re driving transformation across industries with smart, sustainable systems that enable everything from AI, industrial automation, and data centers to EV charging, renewable energy, and intelligent buildings.
Delta is powering the AI age with cutting\-edge infrastructure solutions for hyperscale and edge data centers. Our high\-efficiency power systems, thermal management, and intelligent monitoring technologies help the world’s largest data operators achieve peak performance and sustainability. Join us to help build the cornerstone solutions that will run tomorrow’s digital world. Role summary:Delta's in\-house AI and market intelligence expert. This person is the team's authority on how the AI\-driven data center market is evolving, runs competitive and market research, sizes our opportunities, and tracks how the major players (Google, NVIDIA, the hyperscalers) are reshaping data center architecture and demand. What it does for Delta: turns a fast\-moving AI infrastructure market, chip roadmaps, power and cooling architecture shifts, and hyperscaler strategy, into TAM, SAM, and competitive insight that guides the product and go\-to\-market decisions of the whole team. Ideal profile: an analyst with deep AI and data center market fluency, strong research and data skills, and the ability to translate fast\-moving technology and competitive shifts into clear guidance for product strategy. We especially value hands\-on AI experience, someone who has used AI heavily in practice, including to build or write code, which signals real depth with the technology.
Key Responsibilities:
What You'll Do* Serve as the team's AI expert and run AI research on the data center infrastructure market.
- Track how data center architecture is changing, power, cooling, compute, and networking, and what it means for our portfolio.
- Analyze how major players (Google, NVIDIA, hyperscalers, and key competitors) are reshaping the market.
Market \& Competitive Intelligence* Research competitors, their products, roadmaps, and positioning.
- Build and maintain competitive intelligence for the product and sales teams.
- Track industry, technology, and economic trends relevant to the portfolio.
Market Sizing \& Analysis* Develop TAM and SAM models for our products and target segments.
- Support multi\-year market forecasts and opportunity prioritization.
- Source and validate market data from credible, approved industry sources.
Insight \& Enablement* Turn research into clear briefings, models, and recommendations for the product team.
- Support product, pricing, and go\-to\-market decisions with data and analysis.
- Maintain a shared intelligence base the whole team can draw on.
Key Skills \& Capabilities* Deep understanding of AI infrastructure and how the data center market is evolving.
- Strong research, data analysis, and market\-sizing skills.
- Ability to make sense of fast\-moving technology and competitive shifts.
- Excellent communication and the ability to brief technical and executive audiences.
- Knowledge of industry trends, customer needs, and competitors.
Minimum Qualifications:
- Bachelor's degree in Business, Engineering, Economics, Data Science, or a related field.
- 3\+ years in market intelligence, competitive analysis, research, or a related analytical role.
- Knowledge of the AI and data center infrastructure market, including:
- AI compute and accelerators (for example NVIDIA) and their impact on infrastructure
- Hyperscaler and cloud provider strategy (for example Google and peers)
- Power, cooling, and data center architecture trends
- Strong data analysis skills and experience building market models (TAM and SAM).
- Ability to work independently and turn ambiguity into clear insight.
- Strong presentation skills for technical and executive audiences.
- Strong hands\-on experience using AI tools in practice. Experience using AI to build or write code is strongly preferred, as a signal of deep, practical fluency with AI.
- Proficiency with Microsoft Office (PowerPoint, Excel, Word); data and AI tools a plus.
- Detail\-oriented, results\-driven, and proactive.
- Willing to travel domestically and internationally as needed.
\#LI\-AL1
Pay Range: USD $80,000\.00 \- USD $120,000\.00 /Yr. Benefit at Delta Electronics Americas:
Life at Delta
EEO Statement:
Delta Electronics Americas is an Equal Opportunity Employer and is committed to compliance with all applicable federal, state, and local laws. Delta Electronics America is committed to fostering a diverse, inclusive, and respectful workplace. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity or expression, age, marital status, national origin, disability, veteran status, or any other protected characteristic.
Salary Context
This $80K-$120K range is in the lower quartile 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 National Louis University, 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. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($100K) sits 53% below the category median. Disclosed range: $80K to $120K.
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.
National Louis University AI Hiring
National Louis University has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Plano, TX, US. Compensation range: $120K - $120K.
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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