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Explainable Ai Jobs in Arizona (NOW HIRING)

State Farm

Lead GenAI Engineer (LLM)

Tempe, AZ · Hybrid

$98K - $129K/yr

We work at the frontier of large language models, agentic architectures, and cloud-native AI -- delivering solutions that are reliable, explainable, and that enhance human decision making. Join us if ...

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Explainable Ai information

What is the difference between Explainable Ai vs Data Scientist?

AspectExplainable AiData Scientist
CredentialsTypically requires knowledge of AI, machine learning, and data analysis; certifications like AI or ML courses are commonRequires degrees in computer science, statistics, or related fields; certifications in data analysis or machine learning are beneficial
Work EnvironmentWorks within AI development teams, focusing on model transparency and interpretabilityWorks across data analysis, model building, and business insights, often in research or corporate settings
Industry UsageUsed in AI development, healthcare, finance, and any field requiring transparent AI modelsApplied in tech, finance, healthcare, and research for data-driven decision making

Explainable Ai focuses on making AI models transparent and understandable, ensuring trust and compliance. Data Scientists develop and analyze models, often working with complex data. While both roles involve AI and data, Explainable Ai specialists emphasize interpretability, whereas Data Scientists focus on model creation and insights.

What are some of the typical challenges faced when working in Explainable AI and how do professionals address them?

Professionals in Explainable AI often encounter challenges such as balancing model accuracy with interpretability, translating complex model outputs into understandable insights for non-technical stakeholders, and ensuring transparency without compromising sensitive data. Addressing these issues typically involves using specialized tools and frameworks for visualization, collaborating closely with data scientists, domain experts, and business teams, and staying updated on the latest research in model interpretability. Continuous learning and open communication are key to overcoming these challenges and delivering AI solutions that are both effective and trustworthy.

What is a $900000 AI job?

A $900,000 AI job typically refers to a high-level position in artificial intelligence, such as senior AI researcher, machine learning director, or AI solutions architect, often requiring advanced skills in data science, programming, and deep learning. These roles usually involve leadership responsibilities, strategic planning, and expertise in tools like Python, TensorFlow, or PyTorch, and may require relevant certifications or advanced degrees. Compensation at this level reflects significant experience and impact within the organization.

What degree is needed for XAI jobs?

Explainable AI (XAI) jobs typically require a bachelor's degree in computer science, data science, or a related field, with many roles preferring or requiring a master's or Ph.D. in artificial intelligence, machine learning, or a similar discipline. Strong programming skills, knowledge of machine learning frameworks, and understanding of model interpretability are also important for these roles.

What is the highest paying AI job?

The highest paying AI jobs typically include roles such as AI research director, machine learning engineer, and AI solutions architect, often requiring advanced degrees and expertise in deep learning, natural language processing, or computer vision. These positions can offer salaries exceeding $150,000 annually, especially in tech hubs or large organizations with specialized AI needs.

What are the key skills and qualifications needed to thrive as an Explainable AI specialist, and why are they important?

To thrive as an Explainable AI specialist, you need a strong background in machine learning, data science, and statistics, typically with an advanced degree in computer science or a related field. Familiarity with frameworks such as TensorFlow, PyTorch, and libraries like LIME or SHAP, as well as experience in model interpretability tools, is essential. Strong analytical thinking, effective communication, and the ability to translate complex technical concepts for non-technical stakeholders are crucial soft skills. These capabilities ensure that AI models are transparent, trustworthy, and can be responsibly integrated into decision-making processes.

What is Explainable AI?

Explainable AI (XAI) refers to methods and techniques in artificial intelligence that make the results of AI models understandable and interpretable by humans. XAI aims to provide transparency into how AI systems make decisions, helping users trust and effectively manage AI applications. This is especially important in fields like healthcare, finance, and law, where understanding the reasoning behind AI-driven outcomes can be crucial for accountability and compliance. By making AI more transparent, XAI also helps identify and address biases or errors in AI systems.

Which 3 jobs will survive AI?

Explainable AI specialists, data scientists, and AI ethics professionals are likely to continue thriving as AI advances, because their roles involve understanding, interpreting, and ensuring transparency of AI systems. These jobs require critical thinking, domain expertise, and communication skills that are difficult to automate fully. Continuous learning and familiarity with AI tools and frameworks are essential for these roles to remain relevant.
What are popular job titles related to Explainable Ai jobs in Arizona? For Explainable Ai jobs in Arizona, the most frequently searched job titles are:
What job categories do people searching Explainable Ai jobs in Arizona look for? The top searched job categories for Explainable Ai jobs in Arizona are:
Explainable Ai jobs near you
Infographic showing various Explainable Ai job openings in Arizona as of July 2026, with employment types broken down into 78% Full Time, 20% Part Time, and 2% Contract. Highlights an 64% Physical, 4% Hybrid, and 32% Remote job distribution.

Technical Architect - Data, Analytics & AI

Munich Re

Goodyear, AZ • Hybrid

$61.75 - $79.50/hr

Other

Medical, Life, Retirement, PTO

Posted 21 days ago

Job description

Location: Princeton, New Jersey Hybrid 40-50% onsite 

Role Overview

We are seeking a Technical Architect (TA) with deep expertise in Data, Analytics, and Artificial Intelligence (AI) to join the IT Enterprise Architecture organization. This role is accountable for proactively leading data, analytics, and AIdriven technology transformation initiatives and enabling measurable business outcomes across the enterprise.

The Technical Architect will play a critical role in transforming local, legacy, datadriven processes, and systems into centralized, scalable, and groupwide platforms, while ensuring alignment with enterprise architecture standards and business strategy.

Technical Architects provide technical leadership across analysis, design, facilitation, and execution, supporting the evolution of enterprise Data, Analytics, and AI capabilities and the associated application portfolios and technology stacks. The role owns the creation of key architectural deliverables such as targetstate architectures, transformation roadmaps, standards, and guidelines to enable successful project delivery and longterm strategic outcomes.

This position is based in the USA and ensures that Data, Analytics, and AI architecture vision, principles, and standards are consistently executed through a common enterprise framework, with a strong emphasis on cloudbased data platforms, AI enablement, and data governance.

The ideal candidate will help advance organizational directives around simplification, modernization, and innovation by providing architectural leadership in enterprise data platforms, integration components, and AIenabled data strategies.

Key Responsibilities

  • Assist in the development of a multiyear Data, Analytics, and AI roadmap, aligned with the Munich Re Target Architecture and Roadmap Development Process, in collaboration with Data & Analytics Enterprise Architects.
  • Drive standardization of Data, Analytics, and AI technology standards, principles, and guidelines across multiple business entities.
  • Define and maintain technical standards for enterprise data management, analytics platforms, and AI enablement capabilities.
  • Design and guide datacentric and AIenabled initiatives, supporting the transition from traditional data architectures to nextgeneration cloud, analytics, and AI platforms.
  • Act as an evangelist and ambassador for enterprise architecture standards including Data Governance. Data Intake and Ingestion. Data Modeling, Data Integration, Analytics and AI lifecycle management
  • Collaborate closely with Business Solutions teams, Technology Architects, and Enterprise Data Architects across initiatives and implementations.
  • Identify technologyrelated business pain points by mapping business capabilities to current platforms, leveraging EA practices and participating in innovation activities, including AI adoption.
  • Enable IT development and infrastructure teams to make informed technology decisions through frameworks, reference architectures, standards, and reusable patterns.
  • Identify technical risks, architectural gaps, and vulnerabilities that could impact project delivery or lead to postrelease defects.
  • Reduce cost and complexity through standardization, reuse, and rationalization of data, analytics, and AI platforms.
  • Partner with EA and TA peers (enterprise, solution, and business architects) to derive the futurestate technology architecture, aligned to business strategy and external trends.
  • Define migration and transformation plans to close gaps between current and target states, in alignment with Business Solutions and Business Technology Architects.
  • Support governance, assurance, and compliance activities to ensure alignment with enterprise architecture standards and policies.
  • Assess and articulate the organizational, skills, process, and financial impact of changes to the application portfolio, data platforms, and AI stack.
  • Define and govern enterprise AI architecture standards, including model lifecycle management, MLOps, and AI platform integration.
  • Ensure responsible and compliant AI adoption, aligned with AI governance, model risk management, data privacy, and security controls.
  • Guide the integration of AI/ML capabilities into analytics platforms, including predictive, prescriptive, and generative AI use cases.
  • Collaborate with Data Science, Engineering, Security, and Risk teams to enable scalable, secure, and explainable AI solutions.
  • Establish architectural patterns for AI model deployment, monitoring, versioning, and retraining in cloud environments.
  • Evaluate emerging AI technologies, tools, and platforms and provide strategic recommendations for enterprise adoption.

 

Your Profile

  • 4+ years of experience in Enterprise Architecture or Technical Architecture.
  • Bachelor's or Master's degree in Computer Science, Engineering, Information Systems, Mathematics, or Business (or equivalent).
  • Strong experience with cloud platforms and services, including:
    • Azure (e.g.; Azure AI Studio, Azure Data Services and tools)
    • AWS  (e.g.; Amazon Bedrock, Sagemaker, Data Services and tools)
    • Databricks
  • Handson experience with enterprise data concepts, including:
    • Data Intake and Ingestion
    • Data Warehousing
    • Data Lakes / Lakehouse architectures
    • ETL / ELT
    • Interactive and operational reporting
    • Statistical and regulatory reporting
    • Master Data Management (MDM)
    • Data Governance, Quality, Security, Audit, Balance & Control
  • Solid understanding of enterprise architecture practices, including:
    • Architectural patterns
    • Roadmaps
    • Architecture Review Boards
    • Solution Design Boards
  • Experience defining data management and AI roadmaps, cloudbased services, and reusable architectural patterns.
  • Experience integrating operational data with enterprise data lakes.
  • Strong understanding of data integration challenges and solution patterns.
  • Experience with statistical and data science languages such as Python and R (strong asset).
  • Exposure to AI/ML concepts, including model development, deployment, monitoring, and MLOps (required).
  • Familiarity with Generative AI concepts, AI platforms, and enterprise adoption considerations (strong asset).
  • Strong business acumen with deep understanding of:
    • Financial systems
    • Corporate and backoffice systems
    • Enterprise data management, analytics, and AI technology landscape
  • Strong problemsolving skills, unquestioned integrity, and high collaboration capability.
  • Passion for innovation, continuous improvement, modernization, and change management.
  • Excellent written and verbal communication skills, with the ability to communicate effectively at all levels.
  • High sense of ownership, accountability, and pride in delivered outcomes.

At Munich Re US, we see Diversity and Inclusion as a solution to the challenges and opportunities all around us. Our goal is to foster an inclusive culture and build a workforce that reflects the customers we serve and the communities in which we live and work. We strive to provide a workplace where all of our colleagues feel respected, valued and empowered to achieve their very best every day. We recruit and develop talent with a focus on providing our customers the most innovative products and services.

We are an equal opportunity employer. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.

The Company is open to considering candidates in Princeton, NJ. The salary range posted below applies to the Company's Princeton location.

The base salary range anticipated for this position is $141,800 - $207,900 plus opportunity for company bonus based upon a percentage of eligible pay.  In addition, the company makes available a variety of benefits to employees, including health insurance coverage, an employee wellness program, life and disability insurance, 401k match, retirement savings plan, paid holidays and paid time off (PTO). 

The salary estimate displayed represents the typical salary range for candidates hired in this position in Princeton. Factors that may be used to determine your actual salary include your specific skills, how many years of experience you have and comparison to other employees already in this role. Most candidates will start in the bottom half of the range. 

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