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About This Role
Who We Are Stored Energy Systems (SENS) is an award-winning manufacturer of premium quality power conversion products located in Longmont, CO. In business for over 50 years, SENS is the market leader with a proven track record of providing highly reliable battery chargers, DC power systems, and related products to industrial markets. For more information on our company please see our web site at www.sens-usa.com. View the SENS Product Catalog Summary The Principal AI & Business Process Automation Engineer identifies, designs, develops, implements, and supports technology-enabled improvements across the organization. Reporting to the CIO, this role partners with all business functions to identify opportunities, gain stakeholder alignment, and deliver solutions that reduce manual work and process complexity, improve efficiency and decision-making and support scalable growth through process improvement, AI and automation technologies.
This hands-on role combines business analysis, process improvement, software engineering, and cross-functional collaboration.
This position will evaluate emerging AI and process automation technologies, develop automation workflows, establish governance standards, and drive adoption of AI capabilities throughout the organization.
Responsibilities* Partner with internal clients across Operations, Engineering, Sales, Customer Service, Finance, HR, and other functions to understand business processes, identify operational challenges, define requirements, and translate business needs into actionable technical solutions.
- Deliver assigned business process automation and innovation projects end to end, including discovery, requirements definition, solution design, stakeholder alignment, development, testing, implementation, documentation, support, and continuous improvement.
- Proactively identify bottlenecks, manual processes, excessive handoffs, repetitive work, and other process constraints that limit efficiency or scalability, and develop solutions that measurably improve process performance.
- Establish, deploy, maintain, and improve secure enterprise AI and Large Language Model (LLM) capabilities, including private and on-premises solutions, to protect company data, intellectual property, and customer information.
- Design and develop AI-enabled solutions, including agents, knowledge systems, intelligent workflows, and enterprise integrations that leverage internal data while maintaining appropriate security and access controls.
- Evaluate emerging AI, automation, integration, and related technologies; recommend appropriate tools, standards, and architectural changes; and support implemented solutions through troubleshooting, maintenance, and ongoing enhancement.
Required Qualifications* Bachelor’s degree in Information Technology, Computer Science, Engineering, Business Information Systems, or a related field, or equivalent relevant experience.
- 7+ years of relevant experience in software development, business systems, process automation, systems integration, business process improvement, or related technical work.
- Experience documenting, designing, developing, and implementing technical solutions that automate business processes or integrate business systems.
- Practical experience applying AI and generative AI technologies to business or technical solutions.
- Experience working with enterprise systems, APIs, workflow automation, cloud services, or related integration technologies.
- Strong business analysis and problem-solving skills, including the ability to understand and document business problems, process maps, and workflows then translate them into practical solutions.
- Strong communication and collaboration skills with the ability to work directly with business and technical stakeholders.
Preferred Qualifications* Experience developing AI agents, chatbots, AI-enabled knowledge systems, or other AI automation solutions.
- Experience deploying or maintaining private or on-premises AI and LLM technologies.
- Experience with SQL, Python, JavaScript, or other scripting or programming languages.
- Experience working in a manufacturing environment or with manufacturing operations, sales and sales operations, accounting, finance, or other business functions.
- Experience with Infor SyteLine or similar manufacturing ERP systems.
- Experience with platforms such as HubSpot CRM, StarShip, Power BI/Fabric, SharePoint, or Power Automate.
- Relevant AI, cloud, or automation certifications.
Location: This position is located in Longmont, CO. Base Compensation: $120,000 - $176,000 is the projected base salary range and will be determined by the candidate’s overall qualifications and experience. Bonus: This position is eligible for an annual bonus based on individual and company performance. Closing Date: November 6th. While we expect to keep the position open through this date, the posting may be extended or closed early based on applicant volume and hiring needs. Onsite Perks* Free gym with 24/7 access
- Free Electric Vehicle charging
- Breakfast and Lunch Café
- After hours Grab & Go snacks
- Basketball court
- Walking paths
Benefits* Paid Time Off
- Paid holidays
- Medical insurance
- Dental insurance
- Vision insurance
- Short-term and long-term disability
- Life insurance
- 401(k) plan with immediate vesting
- Employee referral program
- Tuition reimbursement
- Professional development
At SENS, our culture is rooted in a set of core values that reflect who we are and how we work. In a recent 2025 poll, our employees identified the values that best define us: “Innovative”, “Respectful”, “Inviting”, “Flexible”, “Collaborative”, and “Growth Driven”. Our team describes us as a dynamic, forward-thinking company where everyone has the opportunity to thrive.
We pride ourselves on being an equal opportunity employer, fully dedicated to fostering inclusion and diversity. We welcome all applicants, regardless of race, color, religion, sex, sexual orientation, gender identity, genetic information, national origin, age, disability, veteran status, or any other legally protected characteristic.
We hope you're interested in making a difference with us. Even if you don't feel that you meet every qualification listed in this job description, we still encourage you to apply.
Salary Context
This $120K-$176K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Stored Energy Systems, 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($148K) sits 30% below the category median. Disclosed range: $120K to $176K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Stored Energy Systems AI Hiring
Stored Energy Systems has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Longmont, CO, US. Compensation range: $176K - $176K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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