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About This Role
Job Description:
The Principal AI Architect to design and lead the technical strategy for our AI infrastructure, platforms, and systems. In this role, you will own the architectural vision for how we build, deploy, and scale AI solutions across the organization. You'll work at the intersection of AI/ML innovation, systems design, and engineering excellence, partnering with product, data science, and infrastructure teams to build robust, scalable, and responsible AI systems.
This is a high\-impact technical leadership role ideal for someone who is passionate about AI systems, thinks deeply about architecture and design patterns, and is excited about solving complex technical challenges at scale.
What you’ll be doing
What will your essential responsibilities include?
AI Architecture \& Systems Design (40%)
- Own the end\-to\-end technical architecture for AI systems, platforms, and infrastructure
- Design scalable, modular, and extensible AI architectures that support multiple use cases
- Define technical standards, best practices, and design patterns for AI development across the organization
- Evaluate and recommend AI/ML frameworks, tools, and technologies (LLMs, vector databases, orchestration platforms, etc.)
- Design solutions for complex challenges: model serving, real\-time inference, batch processing, multi\-model systems
- Create architecture documentation, diagrams, and technical specifications for cross\-functional teams
- Conduct architecture reviews and provide technical guidance on design decisions
AI Infrastructure \& Platform Development (25%)
- Design and architect AI/ML platforms and infrastructure for scale (training, inference, monitoring, deployment)
- Define MLOps and model lifecycle management practices (versioning, governance, lineage, reproducibility)
- Design data pipelines, feature engineering infrastructure, and data management systems
- Architect solutions for model serving, inference optimization, and latency requirements
- Plan and execute infrastructure upgrades, migrations, and technical debt reduction
- Establish monitoring, observability, and alerting frameworks for AI systems
- Work with DevOps and Infrastructure teams to operationalize AI systems
AI/ML Innovation \& Strategy (15%)
- Stay at the forefront of AI/ML research and emerging technologies
- Evaluate new models, frameworks, and techniques for strategic relevance and business impact
- Design proof\-of\-concepts and pilots for emerging AI technologies (generative AI, multimodal models, agents, etc.)
- Provide technical thought leadership on AI strategy and technologydmap
- Mentor data scientists and engineers on architectural best practices and design patterns
- Contribute to technical strategy discussions with product and business leadership
Technical Leadership \& Collaboration (15%)
- Lead and mentor senior engineers, architects, and technical leads
- Establish technical vision and roadmap aligned with business objectives
- Drive technical decision\-making and architecture governance across AI teams
- Partner with Product to translate business requirements into technical architecture
- Collaborate with Data Science, Engineering, and Infrastructure teams on design and implementation
- Lead design reviews, architecture discussions, and technical problem\-solving sessions
- Build and maintain robust relationships with key technical stakeholders
Responsible AI \& Governance (5%)
- Design systems and processes to ensure responsible AI practices (bias detection, fairness, explainability)
- Establish governance frameworks for model performance, safety, and ethical deployment
- Design monitoring and alerting for model drift, performance degradation, and fairness metrics
- Navigate regulatory requirements and ensure compliance with AI regulations
- Architect solutions for model interpretability, transparency, and auditability
- Lead technical discussions on AI ethics, safety, and responsible system design
You will report to the Global Head of Digital Factory.
What you will BRING
We’re looking for someone who has these abilities and skills:
Required Skills and Abilities:
- Extensive software engineering or systems architecture experience
- Moderate hands\-on experience designing and building large\-scale AI/ML systems
- Proven track record architecting systems that have been deployed to production at scale
- Experience leading technical architecture decisions on complex, mission\-critical systems
- Demonstrated expertise in distributed systems, scalability, and performance optimization
- Deep expertise in AI/ML fundamentals, algorithms, and best practices
- Outstanding understanding of modern ML frameworks and tools (TensorFlow, PyTorch, JAX, etc.)
- Proficiency in at least one programming language (Python, Java, C\+\+, Go, etc.)
- Experience with cloud platforms (AWS, GCP, Azure) and containerization (Docker, Kubernetes)
- Robust understanding of data engineering, ETL pipelines, and data infrastructure
- Knowledge of database systems, data warehousing, and query optimization
- Familiarity with API design, microservices, and distributed system patterns
- Strategic thinking with ability to balance innovation and pragmatism
- Outstanding problem\-solving skills with ability to break down complex technical challenges
- Excellent communication skills; ability to explain complex concepts clearly
- Leadership presence and ability to influence technical teams and stakeholders
- Intellectual curiosity and passion for staying current with AI/ML research and trends
- Comfort with ambiguity and ability to make decisions with incomplete information
- Robust judgment and ability to make sound architectural trade\-offs
- Track record of mentoring senior engineers and technical leads
- Experience navigating organizational and technical complexity at scale
- Thought leadership in AI/ML architecture (speaking engagements, publications, open\-source contributions)
Desired Skills and Abilities:
- Bachelor’s degree in Business, Computer Science, Project Management or a related field. Engineering, computer science, data science, or machine learning background
- Understanding of AI infrastructure, model training, deployment, and MLOps
- Familiarity with prompt engineering, fine\-tuning, and LLM customization
- Knowledge of RAG (Retrieval\-Augmented Generation), agents, and advanced AI architectures
- Experience in enterprise SaaS, B2B, or B2B2C product management
- Track record in regulated industries (finance, healthcare, enterprise) or compliance\-heavy environments
- Previous experience with platform or ecosystem products
- Demonstrated understanding of AI safety, fairness, bias mitigation, governance and responsible AI frameworks
- Knowledge of AI ethics principles and ability to operationalize them in product decisions
- Experience with generative AI, LLMs, or conversational AI products
- Experience with large language models (LLMs), transformers, and generative AI systems
- Expertise in RAG (Retrieval\-Augmented Generation), fine\-tuning, and prompt engineering
- Experience designing AI agents and multi\-step reasoning systems
- Knowledge of model compression, quantization, and inference optimization
- Familiarity with reinforcement learning or other advanced ML techniques
- Experience designing and operating ML platforms (Kubeflow, MLflow, SageMaker, Vertex AI, etc.)
- Expertise in model serving and inference optimization (TensorFlow Serving, Triton, etc.)
- Experience with feature stores and feature engineering platforms
- Knowledge of monitoring, observability, and alerting for ML systems
- Experience with infrastructure\-as\-code and CI/CD pipelines for ML
Who WE are
AXA XL, the P\&C and specialty risk division of AXA, is known for solving complex risks. For mid\-sized companies, multinationals and even some inspirational individuals we don’t just provide re/insurance, we reinvent it.
How? By combining a comprehensive and efficient capital platform, data\-driven insights, leading technology, and the best talent in an agile and inclusive workspace, empowered to deliver top client service across all our lines of business property, casualty, professional, financial lines and specialty.
With an innovative and flexible approach to risk solutions, we partner with those who move the world forward.
Learn more at axaxl.com. What we OFFER
Inclusion
AXA XL is committed to equal employment opportunity and will consider applicants regardless of gender, sexual orientation, age, ethnicity and origins, marital status, religion, disability, or any other protected characteristic. At AXA XL, we know that an inclusive culture and enables business growth and is critical to our success. That’s why we have made a strategic commitment to attract, develop, advance and retain the most inclusive workforce possible, and create a culture where everyone can bring their full selves to work and reach their highest potential. *It’s about helping one another — and our business — to move forward and succeed.*
- Five Business Resource Groups focused on gender, LGBTQ\+, ethnicity and origins, disability and inclusion with 20 Chapters around the globe.
- Robust support for Flexible Working Arrangements
- Enhanced family\-friendly leave benefits
- Named to the Diversity Best Practices Index
- Signatory to the UK Women in Finance Charter
Learn more at Inclusion \& Diversity at AXA XL \| AXA XL. AXA XL is an Equal Opportunity Employer. Total Rewards
AXA XL’s Reward program is designed to take care of what matters most to you, covering the full picture of your health, wellbeing, lifestyle and financial security. It provides competitive compensation and personalized, inclusive benefits that evolve as you do.
We’re committed to rewarding your contribution for the long term, so you can be your best self today and look forward to the future with confidence. Sustainability
At AXA XL, Sustainability is integral to our business strategy. In an ever\-changing world, AXA XL protects what matters most for our clients and communities. We know that sustainability is at the root of a more resilient future. Our 2023\-26 Sustainability strategy, called “Roots of resilience”, focuses on protecting natural ecosystems, addressing climate change, and embedding sustainable practices across our operations.
Our Pillars:* Valuing nature: How we impact nature affects how nature impacts us. Resilient ecosystems \- the foundation of a sustainable planet and society – are essential to our future. We’re committed to protecting and restoring nature – from mangrove forests to the bees in our backyard – by increasing biodiversity awareness and inspiring clients and colleagues to put nature at the heart of their plans.
- Addressing climate change: The effects of a changing climate are far\-reaching and significant. Unpredictable weather, increasing temperatures, and rising sea levels cause both social inequalities and environmental disruption. We're building a net zero strategy, developing insurance products and services, and mobilizing to advance thought leadership and investment in societal\-led solutions.
- Integrating ESG: All companies have a role to play in building a more resilient future. Incorporating ESG considerations into our internal processes and practices builds resilience from the roots of our business. We’re training our colleagues, engaging our external partners, and evolving our sustainability governance and reporting.
- AXA Hearts in Action: We have established volunteering and charitable giving programs to help colleagues support causes that matter most to them, known as AXA XL’s “Hearts in Action” programs. These include our Matching Gifts program, Volunteering Leave, and our annual volunteering day – the Global Day of Giving.
For more information, please see Sustainability at AXA XL. (If needed) Applicants for this role must be legally authorized to work in the United States without sponsorship now or in the future.
The U.S. base salary range for this position is USD 141,000 \- 222,200\.
Actual pay will be determined based upon the individual’s skills, experience and location. We strive for market alignment and internal equity with our colleagues’ pay.
At AXA XL, we know how important physical, mental, and financial health are to our employees, which is why we are proud to offer benefits such as a competitive retirement savings plan, health and wellness programs, and many other benefits. We also believe in fostering our colleagues' development and offer a wide range of learning opportunities for colleagues to hone their professional skills and to position themselves for the next step of their careers. For more details about AXA XL’s benefits offerings, please visit US Benefits at a Glance 2026\.
Salary Context
This $141K-$222K range is above the median for AI Architect roles in our dataset (median: $181K across 29 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 4,317 AI roles we're tracking, AI Architect positions make up 1% of the market. At AXA, 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 $237,300 based on 102 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($181K) sits 23% below the category median. Disclosed range: $141K to $222K.
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.
AXA AI Hiring
AXA has 3 open AI roles right now. They're hiring across AI/ML Engineer, AI Architect. Positions span New York, NY, US, Hartford, CT, US. Compensation range: $222K - $250K.
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 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 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).
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 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
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