Interested in this AI/ML Engineer role at Siemens Energy?
Apply Now →About This Role
A Snapshot of Your Day
As an AI Strategy Consultant, you will help shape and accelerate Siemens Energy’s AI agenda in North America by working at the intersection of strategy, digital enablement, and business execution. Your day may begin with a working session with a business or functional leader to identify high\-value AI use cases, followed by hands\-on collaboration to design, build, or refine Copilot agents, workflows, and strategy\-enablement solutions. Throughout the day, you will partner with leadership, IT, and business teams to translate opportunities into practical solutions, while also helping drive adoption through strong stakeholder management, executive communication, and structured execution. This is a highly entrepreneurial role for someone who can think strategically, operate independently, and turn ideas into tangible business impact.
How You’ll Make an Impact
- Partner with North America strategy leadership, business heads, and functional leaders to identify, prioritize, and develop AI\-enabled use cases that create measurable business value.
- Co\-create, configure, and help deploy Copilot agents, workflows, and related GenAI solutions in support of strategy, decision\-making, and business execution and prepare clear, concise, and executive\-ready updates, presentations, and recommendations for North America leadership.
- Act as a trusted advisor to internal customers, especially senior stakeholders, by translating business needs into scalable AI\-enabled solutions with clear value propositions, implementation paths, and adoption plans.
- Drive execution discipline across a growing backlog of AI opportunities by structuring workstreams, managing priorities, and ensuring timely progress from concept through implementation.
- Bring hands\-on expertise in integrating multiple enterprise data sources and systems, such as SharePoint, OneDrive, SAP, SFDC, and third party platforms especially capturing market, competitive or strategic intelligence, into practical AI\-enabled workflows and solutions.
- Support the development of AI thought leadership within Siemens Energy North America by advising leaders on emerging opportunities, practical applications, and adoption approaches and Enable broader organizational capability by coaching leadership teams and business users on how to build, adopt, and scale Copilot\-based solutions effectively.
What You Bring
- Bachelor’s degree in Business, Engineering, Computer Science, Information Systems, or a related field required; Master’s or PhD preferred.
- A combined experience of 2\+ years in management consulting, strategy, digital transformation, AI enablement, or a comparable business‑facing technology role; well, ‑suited for a strong individual contributor (AI senior consultant or project manager level), with flexibility for a more senior profile based on experience and impact.
- Strong self‑starter with the ability to independently structure problems, manage execution, and drive progress in ambiguous, fast‑moving environments.
- Deep knowledge of Microsoft Copilot and GenAI solutions, including use‑case identification, solution design, and hands‑on implementation, paired with excellent project and program management discipline.
- Proven ability to partner with internal customers and senior stakeholders, delivering outcomes in cross‑functional environments through strong business acumen, executive communication, emotional intelligence, and collaborative working style.
- Solid understanding of enterprise data and systems integration, including platforms such as SharePoint, OneDrive, SAP, SFDC, and third‑party tools; willingness to relocate to Orlando, Florida, travel as needed up to 25%.
- Applicants must be legally authorized for employment in the United States without need for current or future employer\-sponsored work authorization. Siemens Energy employees with current visa sponsorship may be eligible for internal transfers.
About the Team
The North America Strategy team works at the center of Siemens Energy’s regional growth agenda, supporting leadership with strategic insight, market development, executive communications, and high\-impact special projects. As a lean and entrepreneurial team, we operate with a high degree of ownership, independence, and collaboration across business areas and functions. This role will help expand our ability to turn AI from a promising capability into a practical enabler of strategy execution, business productivity, and leadership decision\-making.
Who is Siemens Energy?
At Siemens Energy, we are more than just an energy technology company. With \~100,000 dedicated employees in more than 90 countries, we develop the energy systems of the future, ensuring that the growing energy demand of the global community is met reliably and sustainably. The technologies created in our research departments and factories drive the energy transition and provide the base for one sixth of the world's electricity generation.
Our global team is committed to making sustainable, reliable, and affordable energy a reality by pushing the boundaries of what is possible. We uphold a 150\-year legacy of innovation that encourages our search for people who will support our focus on decarbonization, new technologies, and energy transformation.
Find out how you can make a difference at Siemens Energy: https://www.siemens\-energy.com/employeevideo
Rewards/Benefits
- Career growth and development opportunities
- Supportive work culture
- Company\-paid health and wellness benefits
- Paid Time Off and paid holidays
- 401(k) savings plan with company match
- Family building benefits
- Parental leave
https://jobs.siemens\-energy.com/jobs
\#LI\-CDS
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 Siemens Energy, 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 in Demand for This Role
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. Director-level AI roles across all categories have a median of $274,554.
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.
Siemens Energy AI Hiring
Siemens Energy has 3 open AI roles right now. They're hiring across AI Consultant, AI/ML Engineer. Positions span Houston, TX, US, Orlando, FL, 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.