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About This Role
- Location(s):
- Chicago, IL, US, 60654
- Austin, TX, US, 78701
RWE Americas, LLC
To start as soon as possible, full time, permanent
Functional area: IT / Digital
Remuneration: Exempt
The Principal AI Engineer is a platform level technical expert responsible for building and operating the shared AI capabilities that squads and business units build on, and for setting the engineering patterns and reference implementations that other engineers follow. This role owns the technical design, build, and operational decisions for those shared capabilities, working within the platform roadmap set by the head of AI Platform and Products, and the target-state architecture set by the Data/AI Platform Architect.
This role integrates and operates shared platform services including orchestration and agent frameworks, retrieval and knowledge services, evaluation tooling, model and prompt lifecycle management, observability, and guardrails.
The Principal AI Engineer operationalizes responsible AI at the platform level, delivers AI platform cost efficiency, and raises the output of the wider AI organization.
Role Responsibilities:
- Integrate and operate the shared AI platform capabilities squads and business units build on, including orchestration and agent frameworks, retrieval and knowledge services, evaluation tooling, model and prompt lifecycle management, observability, and guardrails. Ensure they are production-ready, supportable, and adopted across squads consistent with the enterprise target-state architecture and platform roadmap
- Solve the enterprise’s hardest AI engineering problems, including retrieval quality against a fragmented document and operational data estate, building useful models where labeled outcomes are scarce, enabling agents to operate safely in compliance-bound workflows, and resolving assets, sites, and other entities across systems with no common identifiers
- Define reference implementations for AI development across squads and drive their adoption, making the engineering standard visible, consistent, and teachable
- Operationalize responsible AI at the platform level through evaluation and guardrail infrastructure, explainability and citation, bias and drift detection, override and audit logging, prompt and model versioning, and the evidence trail required for compliance and audit
- Deliver AI platform cost efficiency through instrumentation and per-product economics, leading structural changes across routing, caching, context management, and model tiering that keep spend proportional to the value delivered, within the enterprise AI FinOps framework
- Multiply the output of the engineering organization by leading design reviews, coaching senior engineers, and evolving platform standards
Job Requirements and Experiences:
- Master’s or PhD degree in computer science, data science, STEM, or related field required
- Minimum 11 years of relevant professional experience in software or AI/ML engineering roles, including demonstrated experience building and operating shared platform capabilities used by multiple engineering teams and setting technical patterns that other engineers follow
- Deep expertise in enterprise-scale AI development, with the ability to set engineering standards that other engineers follow within an established platform architecture
- Production experience with a major cloud platform, Python, containerized deployment and CI/CD for AI services, and current LLM orchestration and agent frameworks
- Extensive experience integrating and operating the shared AI platform services described above in a production enterprise environment, with personal accountability for their reliability and supportability
- Depth in responsible AI engineering, with hands-on ownership of the evaluation, guardrail, and audit infrastructure described above
- Demonstrated ability to drive AI platform cost efficiency using the structural levers described above
- Proven ability to mentor and professionally develop junior AI engineers
- Experience with IT/OT environments, industrial data sources, or operational technology systems preferred
- Experience in the power, utilities, or clean energy sector, including knowledge of U.S. power markets, renewable project development, and clean energy technologies preferred
- This position is an office-based role with some travel and visits to other RWE Americas offices and field locations. The employee must be able to sit, walk, or stand for long durations of time
Applicants must be legally authorized to work in the United States. RWE Americas is unable to sponsor or take over sponsorship of employment visas at this time.
Pay range: The annual base salary range for this position in Illinois is $178,000 - $240,000. The listed salary range represents our good faith estimate for this position and represents the range for new hire salaries across all U.S locations. Please note that the salary information is a general guideline only. RWE considers factors such as (but not limited to) scope and responsibilities of the position, candidate’s education & work experience, training & certifications, and key skills as well as market and business considerations at the time of the offer.
Benefits offered: Medical, Dental, Vision, Life Insurance, Short-Term Disability, Long-Term Disability, 401(k) match, Flexible Spending Accounts, EAP, Education Assistance, Parental Leave, Paid time off, and Holidays. Eligible employees also participate in short-term incentives, in addition to salary.
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Any questions? Contact HR: [email protected]
We look forward to meeting you. Of course, you can find us on LinkedIn, Instagram, Facebook, YouTube and Xing, too.
All qualified applicants will receive consideration for employment without regard to race, color, sex, sexual orientation, gender identity, religion, national origin, disability, veteran status, or other legally protected status.
RWE Americas, a subsidiary of RWE, is a US-based energy company that is helping to meet the growing demand for energy across the United States. Backed by RWE’s 125-year global legacy of managing diverse power assets, RWE Americas operates approximately 13 GW of power projects across 27 states. With a talented workforce of 2,000 employees, RWE Americas develops, constructs and operates wind, solar and battery storage projects, along with natural gas-powered generation, that safely deliver affordable, reliable electricity to our customers. Committed to responsible development, RWE Americas invests in local and rural communities, creating jobs and partnering with stakeholders to support and strengthen the places where we live and work. Learn more about how RWE Americas is generating impact at americas.rwe.com.
At RWE Americas, we foster a culture defined by our Essential Behaviors - Have Courage, Create Impact and Actively Collaborate. We encourage bold thinking and continuous learning, and we value ownership, resilience and inclusion in everything we do. When you join us, you become part of a team that supports your development, respects your contributions and celebrates shared success. This is a place where you can grow your career and make a meaningful difference.
Salary Context
This $178K-$240K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At RWE, 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 $212,000 based on 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. Disclosed range: $178K to $240K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
RWE AI Hiring
RWE has 2 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer. Positions span Austin, TX, Chicago, IL, US. Compensation range: $240K - $240K.
Location Context
AI roles in Chicago pay a median of $194,700 across 390 tracked positions. That's 9% 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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