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About This Role
The Company
Cypress Creek Energy is powering a sustainable future, one project at a time. We develop, finance, own and operate utility\-scale and distributed solar and storage projects across the country. Fostering a diverse group of innovative thinkers from all backgrounds, Cypress people are drawn to work in a purpose\-driven organization. We hope you will join us.
Overview
The Jr. AI Automation Analyst / Engineer is a hands\-on builder who helps reimagine how business functions work in an AI\-first world. This role sits at the intersection of business and engineering: you'll dig into how teams operate today, identify where AI can replace, augment, or accelerate the work, and then build the automations that make it real. As part of a growing AI team, you'll partner with senior AI engineers and business stakeholders to design, deploy, and maintain solutions that improve efficiency, accuracy, and scale across Cypress Creek Energy.
This role combines technical execution, process analysis, and enterprise AI leadership.
Responsibilities
AI Automation Delivery (Individual Contributor)
- Evaluate business functions ripe for AI \& Automation – Critical thinking and "outside the box" is key
- Enable and train end\-users with the latest AI tools enhancing workflows
- Design, build, and support AI\-enabled automations using low\-code, RPA, APIs, and AI services
- Implement document intelligence, data extraction, classification, and workflow automation solutions
- Integrate AI automations with ERP, HRIS, Finance, Accounting, Tax, Legal, and other enterprise systems
- Develop scripts and logic (e.g., Python, JavaScript) to support automation workflows
- Monitor, troubleshoot, and optimize automations for reliability and performance
AI Strategy
- Partner with business and IT leaders to identify, prioritize, and govern AI use cases
- Establish standards for responsible AI use, security, data handling, and compliance
- Maintain an enterprise AI roadmap aligned to business value and risk tolerance
- Ensure visibility and transparency into AI initiatives across the organization
Business Partnership \& Analysis
- Work directly with stakeholders to understand processes and opportunities
- Translate business requirements into technical automation designs
- Document workflows, data flows, controls, and dependencies
- Measure and report automation outcomes (time saved, cost reduction, accuracy improvements)
Governance, Risk \& Controls
- Ensure AI automations comply with security, privacy, and access control standards
- Implement monitoring, logging, testing, and change management practices
- Support audit, compliance, and risk review activities related to AI solutions
Education \& Experience Required
Technical
- Familiarity with Claude (\+Platform), AWS, Microsoft CoPilot, Power Automate, ChatGPT, Zapier, Agentic AI
- Experience building or configuring Claude Enterprise systems and platforms
- Familiarity with Microsoft 365 integrations and SharePoint, Teams (AI Bots), or Dynamics workflows
- Hands\-on experience with automation platforms (Power Automate, UiPath, Automation Anywhere, Zapier, Make, n8n, etc.)
- Experience integrating SaaS systems using APIs and webhooks
- Familiarity with AI/ML services (LLMs, OCR, document intelligence, NLP)
- Working knowledge of Python, JavaScript, or similar scripting languages
- Understanding of enterprise data formats and basic SQL
Leadership \& Communication
- Experience leading cross\-functional working groups or committees
- Thrives in fast\-paced environments with a fail fast and iterative approach mindset
- Strong communication and facilitation skills
- Comfort presenting to technical and non\-technical stakeholders
- Embrace and live by the mission and values of Cypress Creek Energy
Preferred Qualifications
- Experience developing AI governance frameworks or standards
- Experience with enterprise automation governance programs
- Experience with AI\-assisted tools or workflow augmentation
- Ability to create documentation and user training materials
- Experience with ERP, AP, HRIS, or financial systems (NetSuite, Oracle, Salesforce, Workday, ADP)
Location: The preferred location for this role is either Durham, NC office or Washington, DC. Our team operates on a hybrid schedule, with in\-office schedule of three days per week.
Compensation: The salary range for the position is $100,000\-$120,000k plus bonus and benefits. Compensation may vary outside of this range depending on a number of factors, including a candidate's qualifications, skills, competencies and experience, and location.
Benefits:
- 15 days of Paid Time Off, accrual up to 20 days, 11 observed holidays.
- 401(k) Match
- Comprehensive package including medical, dental, vision and health insurance
- Wellness stipend, family planning stipend, and generous parental leave
- Tuition Reimbursement
- Phone Bill Reimbursement
- Company Swag
A note to Recruiting Agencies Cypress Creek Energy Human Resources team does not accept unsolicited resumes from third party recruiters, staffing firms, or related agencies. The Human Resources team coordinates all recruiting and hiring at our company. We do not accept resumes from third\-party recruiters unless authorized by the Human Resources team and if a signed agreement is in place. Any unsolicited resumes will be considered property of CCE and we are not responsible for any related fees. All communication related to recruiting partnerships should ONLY be directed to the Human Resources team.
Cypress Creek Energy is an equal opportunity employer and considers all qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, or veteran status. We are committed to providing a workplace that is inclusive and values diversity, and we encourage candidates from all backgrounds to apply.
*Please be aware of recruiting scams—official communications will only come from @ccrenew.com, we will never request personal or financial information, and any suspicious activity should be reported to* *[email protected]**.*
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Cypress Creek Renewables, 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 $181,170 based on 12,692 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $97,880.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Cypress Creek Renewables AI Hiring
Cypress Creek Renewables has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Durham, NC, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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