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About This Role
About the role
As the Business Analytics Developer \& AI Integrator, you’ll design, develop, and operationalize advanced analytics and AI\-enabled solutions across the enterprise. Blending deep technical skill with business acumen, you’ll accelerate data\-driven decision\-making, automate complex workflows, and embed artificial intelligence into core business processes. It’s a role for a hands\-on technologist who can translate business strategy into scalable analytical capabilities that deliver measurable impact.
What you'll do
- Strategy \& innovation — Support TERREPOWER’s analytics and AI strategy, identify high\-value opportunities for analytics, automation, and AI, and advise leadership on emerging technologies and trends.
- Analytics development \& data engineering — Architect dashboards, predictive models, and automated reporting; build and maintain scalable data pipelines and integrations across enterprise systems and cloud platforms; and embed data quality, governance, and security into every workflow.
- AI integration \& automation — Design and deploy AI\-driven solutions — machine learning models, NLP tools, intelligent agents, and decision\-support systems — and build automation frameworks using RPA, low\-code platforms, and custom AI\-powered tools.
- Cross\-functional collaboration — Partner with business units to define requirements and deliver tailored solutions, work with IT, data governance, and cybersecurity teams on deployment and lifecycle management, and run enablement programs that elevate data literacy and AI adoption.
- Performance measurement \& optimization — Establish KPIs and success metrics for analytics and AI initiatives, and continuously optimize models, dashboards, and workflows for accuracy, performance, and user experience.
What you'll bring
Must\-haves
- Bachelor’s or master’s degree in data science, Computer Science, Information Systems, Engineering, or a related field.
- 7\+ years in analytics development, data engineering, or AI/ML solution delivery, with a track record of leading enterprise\-level initiatives with measurable impact.
- Proficiency in analytics and BI platforms (Power BI, Tableau, Qlik, etc.).
- Strong experience with SQL, Python, R, or similar languages.
- Hands\-on experience with cloud ecosystems (Azure, AWS, GCP) and modern data architectures.
Nice\-to\-haves
- Familiarity with machine learning frameworks, LLMs, and AI integration patterns.
- Experience with automation tools (Power Automate, UiPath, Automation Anywhere).
- Experience in manufacturing, distribution, or industrial environments.
- Demonstrated ability to lead cross\-functional initiatives and influence senior stakeholders.
What we offer
- Compensation — Competitive base salary plus annual performance bonus.
- Health \& wellness — Medical, dental, and vision coverage.
- Retirement — 401(k) with company match.
- Time off — Paid time off, holidays, and parental leave.
About Us:
For over 35 years, TERREPOWER (formerly BBB Industries) has been a leader in sustainable manufacturing, driving the circular economy by extending the life of essential products in the automotive and industrial markets.
Founded in 1987 in Daphne, Alabama by the Bigler family, TERREPOWER began as a small regional remanufacturer of starters and alternators. Our commitment to quality and innovation quickly earned us a reputation as a trusted name in the automotive industry.
Recognizing new challenges and opportunities, in 2019 we set our sights on EV battery upcycling\-addressing one of the industry’s most pressing issues. Our engineering team pioneered solutions to extend EV battery life, pushing the boundaries of sustainable innovation.
Recognizing that demand for solar panels would exceed availability and 100,000 tons of waste would potentially go into landfills by 2035, our team searched for an innovative way to upcycle solar panels. With an eye on the future, we developed ways to upcycle solar panels.
Rooted in family and community values, we’re proud to have second\-generation employees contributing to our legacy. Backed by Clearlake Capital, we’ve expanded our footprint into Europe with facilities in Spain, Italy, Denmark, Germany and Poland. We now sustainably manufacture and supply an assortment of nondiscretionary repair parts across more than 90 countries.
As we move forward under the TERREPOWER name, we remain committed to the same values that have always defined us: entrepreneurship, teamwork, customer\-centered, sustainability, safety.
Why Join Us?
When you join our team, you become part of a company that is redefining how essential products are made, reused and repurposed to reduce waste and maximize resources.
Here’s what sets us apart:
- Purpose\-Driven Work – Every day, your work will contribute to extending the useful life of essential products, keeping vehicles on the road and critical systems running.
- Innovative Mindset – We encourage creative problem\-solving and bold ideas to push the boundaries of what is possible.
- Global Reach, Local Impact – With operations in North America and Europe, we have a global presence but remain deeply connected to the communities we serve.
- Growth \& Development – Whether you’re on the production floor, in engineering, or part of our corporate team, we invest in your success through training, mentorship, and career advancement opportunities.
- A Culture of Collaboration – Rooted in teamwork and shared values, our employees work together to tackle challenges and drive meaningful change.
TERREPOWER is an Equal Opportunity Employer. We are committed to fostering an inclusive, diverse, and equitable workplace. We welcome applicants of all backgrounds and do not discriminate on the basis of race, color, sex, pregnancy, age, veteran status, religion, national origin, genetic information, disability unrelated to the ability to perform a job, sexual orientation, or transgender status to the extent protected by law. We believe that diversity drives innovation and success.
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At TERREPOWER, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
TERREPOWER AI Hiring
TERREPOWER has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Dallas, TX, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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