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About This Role
Invenergy is North America’s largest privately held developer, owner, and operator of power infrastructure. With 25 years of trusted execution, we deliver reliable, affordable energy through a diverse portfolio that includes natural gas, solar, land\-based wind, energy storage, transmission, and domestic manufacturing. Headquartered in Chicago, we develop, own, and operate large scale projects that power communities and support the energy future.
This position will be open for application for at least 3 calendar days from the posting date. This position will remain open for application based on business need, which may be before or after the 3\-day posting window.
Job Description
Position Overview
The Manager, Applied AI (Enterprise Analytics \& AI) is responsible for leveraging enterprise AI tools and platforms to design, build, and deploy practical AI solutions that deliver measurable business impact across the organization. This role works hands\-on with individuals and teams to understand their needs, build low\-code AI agents and other custom solutions, and enable teams to integrate AI into their workflows effectively and responsibly.
This position serves as an organizational subject\-matter expert on enterprise AI platforms, including their capabilities, limitations, and best practices. The role leads the co\-development of AI agents and other AI solutions while fostering a culture of responsible AI use and self\-sufficiency across the organization.
Responsibilities
- Design, build, deploy and manage low\-code AI agents and other complex custom AI solutions, such as multi\-agent systems or advanced process automation, in partnership with business teams to address specific organizational needs and use cases.
- Lead engagement with business teams in one\-on\-one and small\-group settings to understand workflows, identify AI opportunities, and co\-develop effective, responsible solutions.
- Maintain deep familiarity with the full range of capabilities, limitations, and evolving features of the organization's enterprise AI platforms to ensure solutions are thoughtfully designed and maximize available functionality.
- Oversee the evaluation of third\-party AI software applications, assessing organizational fit, capability, security, and responsible AI considerations, and providing recommendations to inform Business Operations decisions.
- Monitor and analyze organizational AI usage and adoption data; identify trends, gaps, and opportunities, and recommend targeted actions to improve utilization and impact.
- Partner with the Talent Development team to design, deliver, and continuously improve training content and guidelines that help employees use AI tools effectively and responsibly and support self\-service adoption across the organization.
- Stay current on emerging AI capabilities and platform updates, proactively recommending features and solutions relevant to the organization's evolving needs.
- Collaborate with Enterprise Analytics \& AI data scientists, data engineers, and analytics teams to align end\-user AI solutions with broader AI initiatives.
- Manage one or more employees supporting AI adoption functions at the organization.
Required Qualifications
- Bachelor's degree in a technical or quantitative field such as computer science, information systems, engineering, data science, or a related discipline.
- 8\+ years of experience within the industry or in a technology function, with significant achievements and upward growth.
- Demonstrated experience designing, building, or managing AI agents or similar AI\-driven solutions.
- Strong familiarity with the Microsoft ecosystem, including Microsoft 365 and AI\-enabled productivity tools.
- Prior experience leading, mentoring, or managing direct reports, or demonstrated readiness to step into a people leadership role.
- Ability to communicate complex technical concepts clearly to non\-technical audiences, including presenting to senior leadership.
- Strong independent time\-management and project\-management skills, with the ability to manage multiple initiatives simultaneously.
- Proven ability to collaborate effectively across business, technical, and enablement teams.
- High level of curiosity, enthusiasm for emerging technologies, and a continuous\-learning mindset.
Preferred Qualifications
- Experience managing employees in a technology function.
- Direct, hands\-on experience with Microsoft Copilot Studio, including designing and deploying enterprise AI agents or low\-code AI solutions.
- Experience supporting enterprise technology adoption, enablement, or change initiatives.
- Experience developing or delivering technical training or learning content for broad employee audiences.
- Familiarity with responsible AI practices, governance, security, or compliance considerations in an enterprise environment.
- Experience partnering with data science, analytics, or engineering teams to translate advanced AI capabilities into user\-facing solutions.
- Exposure to Microsoft Power Platform, Azure AI services, Databricks, or related tools that complement enterprise AI solutions.
- Proficiency in coding languages such as Python or SQL.
Base Pay
134,000\.00 \- 155,000\.00 USD Annual
Bonus: 25% \- 40%
The base pay range reflects the minimum and maximum target salary for the position. Invenergy considers a number of factors when determining base pay offers such as the scope and responsibilities of the position and the candidate's experience, education and skills.
In addition to base pay, the total annual compensation package may also include eligibility to participate in our bonus program(s) which are designed to reward individual and company performance. Your recruiter can share more about bonus eligibility for this position during the hiring process.
Invenergy offers a variety of other benefits including medical, dental and vision insurance, 401k, paid time off, etc.
Invenergy LLC is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to, among other things, race, color, religion, sex, sexual orientation, gender identity, national origin, age, status as a protected veteran, or disability.
Salary Context
This $134K-$155K range is below the median 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 Invenergy, 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. This role's midpoint ($144K) sits 33% below the category median. Disclosed range: $134K to $155K.
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.
Invenergy AI Hiring
Invenergy has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Chicago, IL, US. Compensation range: $155K - $155K.
Location Context
AI roles in Chicago pay a median of $192,900 across 197 tracked positions. That's 10% below the national 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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