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About This Role
Product Engineering develops and establishes many of the processes used throughout product development, qualification, and manufacturing. As product complexity, engineering data, and production volumes continue to grow, these processes must evolve into connected, traceable, and AI-ready systems.
The Senior Principal AI Architect establishes technical direction and drives adoption of AI/ML and automation across OMG. The role works across engineering functions and with AI/ML experts throughout Nokia to develop shared methods, reusable solutions, governance frameworks, and practical capabilities that improve engineering effectiveness throughout the product lifecycle.
Success is measured by organizational adoption, development of engineering capability, delivery of scalable solutions, measurable improvement in engineering practice, and influence of AI/ML methods across Nokia engineering organizations.
- Lead OMG AI/ML and automation strategy, architecture, technical direction, priorities, and adoption across PIC, ASIC, TROSA, and Pluggable technologies.
- Build AI/ML capability across OMG through technical guidance, mentoring, reusable examples, and coordinated learning opportunities.
- Establish common methods, governance, knowledge systems, and reusable architecture patterns for scalable, traceable, and responsible use of AI/ML.
- Transform engineering data, workflows, knowledge, and traceability into AI-ready systems spanning development, qualification, manufacturing, reliability, and field performance.
- Guide engineers in the selection, development, validation, and use of AI/ML methods and reusable solutions, including agents, skills, RAG, analytics, and automation.
- Partner across Development, Product Engineering, Test, Reliability, Quality, Manufacturing, Operations, IT, and Nokia AI/ML communities to align methods, platforms, architecture, and best practices.
- Define and maintain the OMG AI/ML technology roadmap and align it with Nokia AI strategy, architecture direction, and long-term business objectives.
- Represent OMG in Nokia-wide AI, data, automation, and architecture forums, driving alignment and reuse across organizations.
- Establish AI/ML governance, architecture standards, and engineering practices that can be adopted across multiple Nokia engineering organizations.
- Lead evaluation and selection of AI platforms, cloud technologies, external partners, implementation approaches, and strategic technology investments supporting OMG objectives
- Develop OMG AI/ML competency frameworks, workforce development plans, training strategies, and technical communities.
- Serve as a senior technical advisor to OMG leadership on AI/ML opportunities, risks, priorities, and investments.
- Provide strategic leadership for cloud platform initiatives, including project team staffing, contributor selection, priorities, deliverables, risks, and decisions.
- Master's degree or higher in Electrical Engineering, Computer Science, Data Science, Physics, Photonics, Applied Mathematics, or a related field, or equivalent relevant experience.
- Demonstrated organizational and technical leadership in AI/ML, analytics, software, or automation for engineering, product development, test, or manufacturing.
- Strong technical depth in programming, data analysis, and tool development using Python or similar technologies, with the ability to guide teams and review solution approaches.
- Experience with modern AI/ML methods and architectures, including agents, skills, RAG, knowledge systems, engineering data systems, traceability, or decision-support tools.
- Experience with optical transceiver products, photonics, DSP, semiconductor technologies, Product Engineering, or manufacturing and test systems is highly desirable.
- Demonstrated experience defining organization-level technology strategy, roadmaps, and architecture direction across multiple engineering organizations.
- Demonstrated success establishing enterprise-scale technical standards, governance frameworks, or architecture methods.
- Experience leading large cross-functional transformation initiatives involving engineering, operations, IT, manufacturing, or external partners.
- Experience influencing technology investment decisions, platform strategy, build-versus-buy evaluations, and partner selection.
- Experience building technical communities, competency-development programs, workforce plans, or AI/ML enablement programs.
- Ability to communicate AI/ML strategy, opportunities, risks, and investment recommendations to senior and executive leadership.
- Recognized technical authority in AI/ML, engineering automation, knowledge systems, or digital engineering transformation.
- Experience evaluating technical talent, forming teams, and working with internal and external delivery partners.
- Experience leading cloud-based AI/ML, data, or engineering platform initiatives.
- Experience defining project scope, priorities, deliverables, risks, and stakeholder alignment across organizational boundaries.
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 9,653 AI roles we're tracking, AI Architect positions make up 1% of the market. At Nokia, 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 $230,000 based on 212 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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: $272,275; VP: $235,000.
Nokia AI Hiring
Nokia has 4 open AI roles right now. They're hiring across AI/ML Engineer, AI Architect. Positions span US, Coppell, TX.
Location Context
AI roles in Austin pay a median of $207,800 across 271 tracked positions. That's 3% below the national 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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).
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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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