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Patterned Learning Ai Jobs in Arizona (NOW HIRING)

Staff / Principal AI Engineer

Gilbert, AZ · On-site

$170K - $190K/yr

... learning, natural language processing, large language models, embeddings, retrieval patterns, and ... Experience designing AI or ML solutions in Azure or comparable cloud environments * Experience with ...

Lay the architectural groundwork for future Artificial Intelligence and Machine Learning (AI/ML ... Define API strategy, governance, and system integration patterns to ensure seamless ...

Lay the architectural groundwork for future Artificial Intelligence and Machine Learning (AI/ML ... Define API strategy, governance, and system integration patterns to ensure seamless ...

Responsibilities : • Review data preparation tasks, and plans to address patterns or anomalies ... learning algorithms, while enhancing analytics including LLMs, and create innovative, cost ...

Google AI Lead Architect

Tempe, AZ · On-site

$53.75 - $73.75/hr

Apply and enforce Application Design Patterns and Agentic Design Patterns to build resilient and ... Preferred : • Google Professional Machine Learning Engineer certification or the equivalent ML ...

... Gemini) including patterns such as RAG, embeddings, vector search, and governed access to ... machine learning models and large language models. • Conduct research to provide technical ...

Senior Applied & Agentic AI Engineer

Flagstaff, AZ · On-site

$102K - $140K/yr

... secure deployment patterns. · Mentor engineers on LLM orchestration patterns, workflow ... machine learning systems, or distributed software architecture. · 3-5+ years designing and ...

Senior Applied & Agentic AI Engineer

Phoenix, AZ · On-site

$103K - $142K/yr

... secure deployment patterns. · Mentor engineers on LLM orchestration patterns, workflow ... machine learning systems, or distributed software architecture. · 3-5+ years designing and ...

AI Data Engineer - Manager

Tempe, AZ

$109K - $131K/yr

Lead the development of AI models (e.g., machine learning, natural language processing, computer ... Gemini) including patterns such as RAG, embeddings, vector search, and governed access to ...

They are seeking an AI Engineer to work alongside data scientists and machine learning engineers to ... patterns • Participate in technology proof of concepts to ensure feasibility of new data and ...

Work with clients to design, develop, and deploy new architectures to support machine learning ... Adopt best engineering practices in automation, HPC and AI/GenAI infrastructure and design patterns

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Patterned Learning Ai information

What are some typical challenges faced by Patterned Learning AI professionals in implementing AI-driven solutions within organizations?

Patterned Learning AI professionals often encounter challenges such as integrating AI models with existing legacy systems, ensuring high-quality and representative training data, and aligning AI solutions with specific business objectives. Collaboration across multidisciplinary teams—including data scientists, software engineers, and business stakeholders—is essential for successful deployment. Additionally, professionals must stay updated on evolving AI technologies and best practices to maintain model accuracy and address ethical considerations.

What are the key skills and qualifications needed to thrive as a Machine Learning Engineer, and why are they important?

To thrive as a Machine Learning Engineer, you need a strong background in mathematics, statistics, programming (especially Python), and a degree in computer science or a related field. Experience with machine learning frameworks such as TensorFlow, PyTorch, and scikit-learn, as well as familiarity with cloud computing platforms and data management tools, is essential. Excellent problem-solving skills, creativity, and clear communication are crucial soft skills for collaborating with teams and translating complex models into practical solutions. These competencies are vital for developing reliable AI systems that solve real-world problems and drive innovation.

What is the difference between Patterned Learning Ai vs Data Scientist?

AspectPatterned Learning AiData Scientist
Required CredentialsTypically requires machine learning, AI, or computer science degrees; certifications in AI toolsRequires degrees in statistics, computer science, or related fields; often certifications in data analysis
Work EnvironmentTech companies, AI startups, research labs focusing on AI developmentBusiness, finance, healthcare, and tech sectors analyzing data for insights
Employer & Industry UsageUsed by AI-focused organizations developing intelligent systemsEmployed across industries for data analysis, predictive modeling, and decision support

Patterned Learning Ai primarily focuses on developing AI models and algorithms, often requiring specialized technical skills. Data Scientists analyze data to extract insights and inform business decisions. While both roles involve data and machine learning, Patterned Learning Ai is more centered on creating AI systems, whereas Data Scientists interpret data for strategic purposes.

What is Patterned Learning AI?

Patterned Learning AI refers to artificial intelligence systems designed to recognize, learn from, and replicate patterns in data. These systems use algorithms to identify trends, correlations, and structures within large datasets, enabling them to make predictions or automate decision-making processes. Patterned Learning AI is commonly used in fields like image recognition, natural language processing, and predictive analytics. Its applications help businesses and researchers uncover hidden insights, streamline operations, and improve accuracy in various tasks.

Staff / Principal AI Engineer

Vaco by Highspring

Gilbert, AZ • On-site

$170K - $190K/yr

Other

Dental, Vision, Retirement

Posted 7 days ago


Job description

Vaco is partnering with a growing education technology organization to hire a Staff / Principal AI Engineer to help define and build the next generation of AI-powered products, platforms, and internal capabilities. This is a highly visible, hands-on technical leadership role focused on bringing practical AI innovation into both customer-facing products and internal business operations.

This person will serve as a technical change leader as the organization moves toward broader use of AI agents, generative AI, machine learning, and LLM-powered product experiences. The right candidate will bring strong architecture and engineering depth, but also a product mindset. This role is about building real solutions, teaching teams how to use AI effectively, securing AI-enabled workflows, and identifying where AI can improve customer experience, training products, marketing funnels, and business outcomes.

What You’ll Be Doing
  • Lead the design, development, and deployment of scalable AI and machine learning solutions across product and internal business use cases
  • Architect generative AI systems, LLM-powered workflows, agentic solutions, and AI-enabled product features
  • Build and optimize LLMOps pipelines that support reliable deployment, evaluation, monitoring, and iteration of AI models
  • Partner with product, architecture, data, engineering, innovation, and marketing teams to identify where AI can create measurable business impact
  • Drive AI innovation within customer-facing products, training platforms, digital experiences, and lead conversion workflows
  • Evaluate and implement approaches for prompt engineering, context management, embeddings, retrieval, and model optimization
  • Design AI systems with feedback loops, automated retraining, fine-tuning, and lifecycle management where appropriate
  • Build data pipelines and preprocessing workflows that ensure data quality, security, and regulatory alignment
  • Provide hands-on technical leadership through architecture reviews, code contributions, proof-of-concepts, and implementation guidance
  • Mentor AI engineers and partner with architects across application, product, CRM, and data domains
  • Help establish standards, documentation, and best practices for responsible AI development and deployment
  • Stay current on emerging AI trends, including AI’s impact on digital marketing, Answer Engine Optimization, Generative Engine Optimization, and customer discovery behavior
Required Experience
  • 8 or more years of progressive experience across software engineering, data, analytics, machine learning, AI engineering, or related technology roles
  • 1 or more years of experience developing and implementing analytical, AI, or machine learning applications
  • Hands-on experience building and deploying LLM-based solutions, generative AI applications, or AI-powered product features
  • Strong understanding of machine learning, natural language processing, large language models, embeddings, retrieval patterns, and model evaluation
  • Experience with Python, SQL, Hugging Face, Snowflake, and modern ML or analytics tooling
  • Experience designing AI or ML solutions in Azure or comparable cloud environments
  • Experience with CI/CD pipelines, Docker, Kubernetes, MLflow, or similar MLOps and lifecycle management tools
  • Ability to translate ambiguous AI opportunities into practical, scalable engineering solutions
  • Strong product mindset with interest in customer experience, product innovation, marketing enablement, and business impact
  • Demonstrated ability to mentor engineers, influence technical direction, and guide best practices
  • Strong communication skills with the ability to present complex AI concepts to technical and non-technical stakeholders
  • Bachelor’s degree in Computer Science, Artificial Intelligence, or a related field preferred, or equivalent professional experience
Nice to Have
  • Experience building AI agents or agentic workflows for internal business operations
  • Exposure to AI security, responsible AI, model governance, or securing AI-enabled systems
  • Experience applying AI to online shopping, customer acquisition, digital marketing, or lead conversion workflows
  • Familiarity with Answer Engine Optimization or Generative Engine Optimization concepts
  • Experience with Azure AI services or comparable cloud AI platforms
  • Background working in education technology, training platforms, eCommerce, or digital learning environments
  • Experience helping organizations adopt AI tools, workflows, and operating models across multiple teams
Compensation & Benefits
  • Salary range: $170,000 to $190,000 annually
  • Full-time role with benefits package available
 

If you are a hands-on AI engineering leader who wants to build practical generative AI systems, shape product innovation, and help an organization move from AI experimentation to real enterprise adoption, we would welcome the opportunity to connect.


Determining compensation for this role (and others) at Vaco/Highspring depends upon a wide array of factors including but not limited to the individual’s skill sets, experience and training, licensure and certifications, office location and other geographic considerations, as well as other business and organizational needs. With that said, as required by local law in geographies that require salary range disclosure, Vaco/Highspring notes the salary range for the role is noted in this job posting. The individual may also be eligible for discretionary bonuses, and can participate in medical, dental, and vision benefits as well as the company’s 401(k) retirement plan. Additional disclaimer: Unless otherwise noted in the job description, the position Vaco/Highspring is filing for is occupied. Please note, however, that Vaco/Highspring is regularly asked to provide talent to other organizations. By submitting to this position, you are agreeing to be included in our talent pool for future hiring for similarly qualified positions. Submissions to this position are subject to the use of AI to perform preliminary candidate screenings, focused on ensuring minimum job requirements noted in the position are satisfied. Further assessment of candidates beyond this initial phase within Vaco/Highspring will be otherwise assessed by recruiters and hiring managers. Vaco/Highspring does not have knowledge of the tools used by its clients in making final hiring decisions and cannot opine on their use of AI products.