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About This Role
Summary
Third party applicants or companies will not be considered, unless directly engaged with by Enabled Energy.
Enabled Energy is seeking an experienced Agentic Application Builder to join our Technology Services team on a contract or contract\-to\-hire basis.
You will build using the modern, AI\-accelerated Microsoft stack — Power Platform, Power Apps Code Apps, Dataverse, Copilot Studio, and agentic development patterns — inside an EE\-owned and governed environment. You will work from conceptual ideas and mockups, Excel\-based data and formula frameworks, and business rules to deliver production\-quality, applications and reporting capabilities.
This role reports to the Director of Technology Services and works closely with Innovation, Business Development, and Technology Services. Successful contractors will be considered for conversion to full\-time employment based on performance and mutual fit.
Key Responsibilities
Design \& Build Rich Digital Experiences
- Translate wireframes, screenshots, scoring logic, lookup tables, and business rules into production\-quality experiences using Power Apps Code Apps.
- Design and implement Dataverse data models (tables, relationships, choices, business rules, AI insights) that support portfolio analysis, sprint execution, and program governance.
- Build reusable UI components, code components, and connectors that accelerate future platform and internal solution delivery.
- Build Power Automate flows and Copilot Studio agents that automate data insights, notifications, workspace provisioning, and client\-facing interactions.
- Deliver experiences that are equally strong for internal team members (business and project developers) and external clients (asset owners, operators, executives).
Support EE Initiatives
- Platform: Build and evolve the application, user interface and experience, data insights, decision support, and reporting capabilities.
- Integrate our platform with Dynamics 365 Sales, Customer Insights, and Dataverse so opportunity, engagement, and client context flow across the platform.
- Contribute to EE's AI\-enabled way of working by implementing agentic development patterns, governed AI experiences, and reusable component libraries.
Agentic Development \& ALM Discipline
- Use VS Code as the primary engineering workspace with GitHub Copilot CLI, Power Platform and Dataverse Skills, and first\-party MCP servers as agentic accelerators.
- Use PAC CLI, source\-controlled solutions, and Azure DevOps for backlog, work\-item management, and GitHub repos, pipelines, and governance.
- Apply repeatable ALM patterns (DEV TEST PROD), environment variables, connection references, and clean solution structure to prevent technical debt.
- Validate work through UAT, documentation, and traceable acceptance criteria; leverage AI to accelerate delivery without sacrificing governance or auditability.
Documentation \& Collaboration
- Produce clean, business\-friendly documentation: solution designs, component libraries, prompt packs, runbooks, and knowledge\-transfer artifacts.
- Collaborate with the Director of Technology Services and other team members on standards, coding conventions, Dataverse schema design, and solution packaging.
Qualifications
Minimum
- 5–8 years of hands\-on application development experience, including at least 2–3 years building on the Microsoft Power Platform (Power Apps, Power Automate, Dataverse).
- Strong Dataverse data modeling skills (tables, relationships, business rules, security roles, ALM).
- Demonstrated ability to build responsive, production\-quality apps from wireframes, mockups, and business logic (canvas apps, model\-driven apps, or Code Apps).
- Proficiency in JavaScript/TypeScript, React, or similar modern web frameworks; authoring code inside VS Code with source control.
- Experience with Power Automate cloud flows, custom connectors, and integration patterns (REST APIs, Graph API, webhooks).
- Experience with Azure DevOps or GitHub (repos, pipelines, work items) and Power Platform ALM.
- Comfort working from business requirements, scoring logic, and data models — not just detailed technical specifications.
Preferred (Plus, Not Required)
- Experience with Power Apps Code Apps and/or comparable low\-code \+ pro\-code hybrid platforms.
- Experience with Copilot Studio, GitHub Copilot, GitHub Copilot CLI, Power Platform Skills, Dataverse Skills, and/or MCP\-enabled workflows.
- Experience building agentic experiences or AI\-assisted development workflows.
- Microsoft development and AI certifications
- Exposure to data center, energy services, engineering, and/or asset management industries.
- Experience with Microsoft Fabric, Power BI, or Dataverse\-linked analytics.
Additional Qualifications
- Execution\-oriented — takes wireframes, concepts, Excel models, and/or business rules and turns them into working, well\-documented software.
- Curious, self\-directed, and energized by building — not just maintaining — new capabilities.
- Moving quickly with AI\-assisted tools while applying judgment, validation, and documentation.
- Deliver high\-quality, maintainable, and reusable components.
- Collaborates well with Sales, Innovation, Delivery, and Technology Services to translate strategy into working software.
Why Join Us
Enabled Energy is a leading consulting and contracting firm specializing in retrofitting existing data centers across the United States and Canada. We are dedicated to delivering innovative, energy\-efficient infrastructure and services that meet the evolving needs of mission\-critical facilities. Our expertise spans energy conservation, HVAC controls, building automation, and system reliability. As we continue to grow, we are seeking passionate professionals to help us shape the future of sustainable data center operations.
What you will get from Enabled Energy
- Mission\-Driven Work: Modernize critical infrastructure, reduce energy use, and enhance reliability
- Career Growth: Clear advancement paths and mentorship
- Employee Experience: Supportive, high\-character culture that values curiosity, collaboration, and creating a great place to work
- Training \& Development: Ongoing learning and industry engagement
- Real Impact: Drive revenue, shape client outcomes, and advance sustainable data center solutions
- Pursuit of Excellence: High standards, thoughtful execution, commitment to quality and continuous learning in all we do
Enabled Energy is an Equal Opportunity/Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, or protected Veteran status class.
Applicants must be authorized to work in the United States on a full‑time basis. We are unable to sponsor or take over sponsorship of employment visas at this time.
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 EnabLED Energy LLP, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
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.
EnabLED Energy LLP AI Hiring
EnabLED Energy LLP has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Littleton, CO, 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
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