Top Artificial Intelligence Jobs in Scottsdale, AZ

Designs, develops, deploys, and supports production AI/ML solutions for healthcare operations. Builds scalable machine learning services, data pipelines, cloud applications, and MLOps workflows; implements CI/CD, monitors and optimizes deployed systems, evaluates emerging AI technologies, and collaborates with product, architecture, data science, and engineering teams. The role also mentors junior engineers and contributes to technical standards, code reviews, and architecture decisions.

Leads the design, development, deployment, and scaling of production AI/ML solutions for healthcare. Builds cloud-based machine learning systems, data pipelines, feature workflows, and MLOps processes including CI/CD, monitoring, drift detection, retraining, and governance. Provides architecture guidance, technical leadership, mentorship, and cross-functional partnership while applying NLP, deep learning, computer vision, foundation models, and other advanced AI techniques.

Design, build, fine-tune, evaluate, and deploy production-grade ML and LLM systems. Implement transformer models, RAG architectures, and agentic workflows; develop MLOps pipelines, containerized deployments, monitoring, and AI evaluation frameworks; collaborate cross-functionally and provide technical leadership and mentorship.

Lead technical vision and architecture for enterprise ML platforms and MLOps. Design training, feature, serving, and observability systems; establish ML lifecycle and governance; solve complex cross-team engineering problems; mentor senior engineers; evaluate emerging AI technologies; drive cost optimization, compliance, and organizational alignment.

Design, build, deploy, and optimize production AI systems, including LLM integrations, RAG pipelines, embeddings, vector search, predictive models, and inference systems. Collaborate with product and engineering teams to translate business needs into scalable solutions for recommendations, risk scoring, forecasting, and workflow automation. Own data pipelines, monitoring, evaluation, security, privacy, documentation, and continuous model improvement across cloud environments.

Design and deliver enterprise AI solutions using agentic AI, LLMs, NLP, RAG, multi-agent systems, and tool orchestration. Partner with stakeholders to identify high-value use cases, define success metrics, and focus on business outcomes. Build scalable, reliable, explainable, and governed systems with evaluation, monitoring, and guardrails. Advise teams on responsible AI adoption, contribute to strategic best practices, and mentor colleagues.

The job description provides limited role details. The AI Fullstack Engineer will presumably help develop AI-powered software solutions for higher education institutions, supporting administrative automation, student engagement, and data-driven decision-making. Specific responsibilities, technologies, qualifications, experience requirements, compensation, travel, and clearance information are not provided.

Leads enterprise strategy, governance, and implementation of Agentic AI across semiconductor new product development. Builds AI-enabled design platforms, reusable engineering agents, knowledge repositories, and governance frameworks spanning analog, digital, verification, layout, testing, packaging, and systems engineering. Drives cross-functional transformation, establishes productivity and quality metrics, oversees AI lifecycle management, and delivers measurable improvements in development speed, engineering efficiency, IP reuse, and design quality.

Evaluates and improves AI-powered customer support agents by analyzing conversation logs, testing prompts, refining knowledge and tools, identifying performance gaps, and recommending model and workflow enhancements. The role partners with Product, Engineering, and Operations leaders, automates recurring support workflows, and helps optimize customer experience, resolution quality, and conversion outcomes.

Design and guide enterprise-scale, cloud-native solutions leveraging software engineering and AI. Provide technical leadership across SDLC, design mobile/cloud apps, integrate via APIs and data services, implement CI/CD and release/configuration practices, and architect end-to-end ML pipelines for cloud and edge deployment while aligning solutions with business objectives.

Lead enterprise AI strategy, governance, and adoption; partner with business and technical leaders to prioritize use cases, measure value and risk, and guide secure, responsible implementation of AI solutions while managing and developing AI talent.

Build and deploy scalable AI and machine learning solutions across business functions, from ideation through production. Lead architecture, MLOps, RAG, prompt engineering, model optimization, integrations, monitoring, observability, and responsible AI governance. Partner with infrastructure, security, data engineering, and business teams while mentoring junior engineers and driving enterprise AI adoption on Azure.

Lead a team to deliver AI/ML solutions, manage projects, ensure compliance, mentor staff, and collaborate with business stakeholders.

Develop and implement AI models and algorithms using Azure technologies, focusing on full-stack development, AI solutions, and collaboration with stakeholders to optimize business processes.

Lead and mentor AI engineers and data scientists to research, prototype, and productionize scalable AI/ML solutions (NLP, CV, predictive analytics, generative AI). Own MLOps, cloud integrations (Azure, AWS, GCP), ethical AI governance, model monitoring/versioning, and cross-functional deployment to meet business objectives.

Design, build, and operate AWS-native AI/ML systems: RAG pipelines, fine-tuned LLMs, and microservices. Implement IaC (Terraform), CI/CD, policy guardrails, and production deployments integrating Bedrock/SageMaker, LangChain, and telemetry for secure, scalable enterprise use.