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About This Role
Here at Siemens, we take pride in enabling sustainable progress through technology. We do this through empowering customers by combining the real and digital worlds. Improving how we live, work, and move today and for the next generation! We know that the only way a business thrives is if our people are thriving. That’s why we always put our people first. Our global, diverse team would be happy to support you and challenge you to grow in new ways. Who knows where our shared journey will take you?
Here at Siemens, we take pride in enabling sustainable progress through technology. We do this through empowering customers by combining the real and digital worlds. Improving how we live, work, and move today and for the next generation! We know that the only way a business thrives is if our people are thriving. That’s why we always put our people first. Our global, diverse team would be happy to support you and challenge everyday with us!
We are looking for Head of Data & AI, Electrical Products. This position will be based at Peachtree Corners, GA.
We are looking for changemakers with the courage to rethink established ways of working. As Head of Data & AI, you will lead the IT Data & AI agenda for Smart Infrastructure Electrical Products (SI EP). Your mission: unlock the full potential of artificial intelligence and modern data architectures for our business and translate them into measurable business value.
In a technology-leading industrial environment, you are a catalyst for change: you ask the right questions, test assumptions with data, and drive innovation forward with vision, collaborative leadership, and close alignment with Global Function IT.
You’ll make a difference with:
- AI strategy and business value – Explore AI opportunities across development, manufacturing, supply chain, sales, and commercial processes, separating viable business cases from short-term hype and translating data and AI capabilities into measurable business value.
- Innovation leadership – Bring in modern best practices from other industries, evaluate emerging technologies, introduce new approaches early, and anchor a data-driven mindset across the organization.
- Team development – Lead an international team of data architects, AI consultants, and developers while building a culture where constructive dissent is valued and experimentation is part of everyday work.
- Portfolio execution – Identify, prioritize, and implement data and AI initiatives with business departments and management, supported by clear business cases, visible ROI, and a consistent fail-fast project culture.
- Architecture, governance, and compliance – Own data and AI architecture while ensuring high standards for data security, compliance, and governance, balancing guardrails with pragmatic ways to shorten innovation cycles.
- IT solutions and platform alignment – Oversee the operation and further development of IT solutions in close coordination with Global Function IT platform owners.
- Budget and resource planning – Manage budget, strategically plan resources, and further develop the organization in line with business requirements.
You’ll win us over by having the following qualifications:
Basic Qualifications:
- Bachelor’s degree in computer science, information systems, data science, or a comparable field.
- 8+ years of experience with the following: + Strategic leadership of data and AI landscapes, including successful implementation of AI concepts into large-scale production operations. + Technical and managerial leadership of international, interdisciplinary teams, including coaching and talent development. + Modern data architectures, including data lake, data warehouse, lakehouse, and data mesh, plus cloud platforms such as Azure, AWS, or Google Cloud. + Machine learning, generative AI, agent-based approaches, enterprise architecture methods, architecture frameworks, and the Microsoft development stack. + Executive-level communication, negotiation, decision-making, and collaboration in international teams.
- Willingness to travel up to 10%, primarily in EMEA/Germany.
- Legally authorized to work in the United States on a continual and permanent basis without company sponsorship.
Preferred Qualifications:
- Master’s degree in computer science, information systems, data science, or a comparable field.
- Experience from dynamic technology companies, modern industrial environments, or strategy/IT consulting.
- Knowledge of global manufacturing processes.
- Experience translating AI and data strategy into measurable ROI and scalable enterprise adoption.
- Experience leading innovation cycles in highly governed enterprise environments while maintaining data security, compliance, and governance standards.
- Experience partnering with business departments, management, and global platform owners to prioritize and deliver data and AI initiatives.
- Fluency in English and German.
#LI-KC1 $145,156 $248,839 20%
Salary Context
This $145K-$248K 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 Siemens, 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. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($196K) sits 7% below the category median. Disclosed range: $145K to $248K.
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.
Siemens AI Hiring
Siemens has 6 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, Research Scientist. Positions span Peachtree Corners, GA, US, Kitchener, Ontario, Canada, Erlangen, Bavaria, Germany. Compensation range: $248K - $248K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 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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