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About This Role
About Siemens EDA
Siemens EDA is a global technology leader in Electronic Design Automation software. Our software tools enable companies around the world to develop highly innovative electronic products faster and more cost\-effectively. Our customers use our tools to push the boundaries of technology and physics to deliver better products in the increasingly sophisticated world of chip, board, and system design.
Position Overview
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We are seeking an experienced AIML Architect to lead the design and evolution of AI\-powered solutions across our design and verification tools. In this strategic role, you'll define technical architecture for AI/ML and Agentic AI systems, establish best practices and governance models, and drive AI platform standardization across our product portfolio. You'll collaborate with engineering leaders, product management, and domain experts to transform innovative AI concepts into production\-grade solutions that shape the future of electronic design automation.
Key Responsibilities
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- Define and drive the technical architecture for AI/ML and Agentic AI solutions across multiple products and teams
- Design scalable AI platforms demonstrating large language models, Agentic Frameworks, Model Context Protocol, LangGraph, vector databases, retrieval\-augmented generation, and emerging AI technologies
- Establish architecture patterns, protocols, governance models, and technology roadmaps for AI\-enabled products
- Lead the design of intelligent agents capable of tool usage, workflow automation, reasoning, planning, and autonomous task execution
- Evaluate and select AI technologies, models, frameworks, and infrastructure to meet product and business objectives
- Architect end\-to\-end AI systems including data pipelines, model serving, observability, memory management, evaluation frameworks, and deployment strategies
- Collaborate closely with engineering leaders, product management, UX teams, AI researchers, and domain guides to deliver production\-grade solutions
- Mentor engineering teams on Genetic AI, large language model architecture, prompt engineering, retrieval\-augmented generation systems, and AI standards
- Define technical strategy for AI adoption and identify high\-impact opportunities across EDA workflows
- Ensure responsible AI practices including model evaluation, governance, security, privacy, and compliance requirements
Qualifications
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Required:
- 12\+ years of proven industry experience with at least 4\-8 years of focused AI/ML work
- Proven experience architecting and delivering enterprise\-scale AI/ML solutions from concept to production
- Strong expertise in large language model\-based systems, Agentic AI frameworks, retrieval\-augmented generation architectures, vector databases, and AI orchestration platforms
- Proficiency in Python with proven understanding of software design patterns and clean code principles
- Hands\-on experience developing AI agents or copilots that collaborate with tools or external APIs
- Understanding of agentic design patterns such as ReAct, plan\-execute, and multi\-step reasoning
- Practical experience with (OpenAI, LLaMA, Claude, etc.) and prompt engineering standard processes
- Hands\-on experience with AI/ML frameworks such as PyTorch, TensorFlow, or Scikit\-learn
- Practical experience in data preprocessing, feature engineering, and model evaluation
- Strong understanding of deep learning models including artificial neural networks and transformers
- Familiarity with developer tooling, VS Code extensions, and copilot\-style integrations
- Ability to identify AI use cases, conceptualize solutions, and drive from prototype to production
- Good foundations in algorithms, data structures, CLI design, and API design
Preferred:
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- Experience with code understanding systems, abstract syntax tree analysis, tree\-sitter, or developer productivity tools
- Proficiency in C/C\+\+
- Experience with graph databases such as Neo4j or FalkorDB
- Background or interest in digital design, SystemVerilog, or EDA tools
- Familiarity with containerization technologies like Docker and CI/CD practices
- Basic knowledge of compilers, parallelism, GPUs, or profiling tools
Why Us
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At Siemens Software, flexibility is how we work—hybrid by default, built on trust and autonomy. Together, 30,000 people across more than 200 countries build technology that shapes the real world. You'll grow through real projects, strong technical peers, and global mobility, backed by the scale and benefits of an industrial software leader. We're committed to equality and inclusion, and we hire based on merit, skills, and impact. Bring your curiosity and creativity and help us shape tomorrow!
Siemens Software. Transform the Everyday
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\#LI\-Hybrid \#LI\-EDA
$150,400 $200,000 15\-20%
Salary Context
This $150K-$200K range is above the median for AI Architect roles in our dataset (median: $169K across 31 roles with salary data).
Role Details
About This Role
This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.
The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.
Across the 3,823 AI roles we're tracking, AI Architect positions make up 1% of the market. At Siemens, this role fits into their broader AI and engineering organization.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
What the Work Looks Like
Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
Skills Required
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.
Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
Compensation Benchmarks
AI Architect roles pay a median of $212,500 based on 108 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($175K) sits 18% below the category median. Disclosed range: $150K to $200K.
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.
Siemens AI Hiring
Siemens has 8 open AI roles right now. They're hiring across AI Architect, AI Software Engineer, AI/ML Engineer, Research Scientist. Positions span Fremont, CA, US, Maryland Heights, MO, US, Princeton, NJ, US. Compensation range: $97K - $267K.
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 Architect roles include Software Engineer, Data Scientist, Data Analyst.
From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.
Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
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).
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
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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