Top Artificial Intelligence Jobs in Scottsdale, AZ

Lead architecture and technical strategy for an agentic AI claims automation platform. Define end-to-end architecture, enable multi-agent workflows, integrate models and MLOps, ensure governance, resilience, observability, and cost efficiency, and mentor architects while aligning with enterprise AI standards.

Leads the enterprise AI governance program, overseeing AI risk assessments, controls, policies, regulatory alignment, committee governance, and executive reporting. Partners with cybersecurity, privacy, legal, compliance, technology, and business teams to enable responsible AI adoption, strengthen governance processes, document risk decisions, and mature the operating model. The role also leads and mentors teams while serving as a credible challenge partner across the organization.

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.

Develop physics-informed AI surrogates and simulation-accelerating models for aerospace mechanical systems. Create scripted FEA/CFD datasets, integrate surrogates into engineering workflows, collaborate on R&D proposals, and advance NVIDIA physics‑accelerated AI tools for design and analysis.

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.

Designs, develops, and deploys AI-powered Salesforce solutions using Agentforce, Apex, Lightning Web Components, Flows, APIs, prompt engineering, and Retrieval-Augmented Generation. Builds autonomous agents and intelligent coaching workflows, integrates Salesforce with external systems, configures CI/CD pipelines, monitors agent performance, and applies enterprise security, governance, and responsible AI standards. Collaborates with architects, stakeholders, product owners, and operations teams in an Agile environment to automate administrative work and improve personalized student engagement.

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.

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 enterprise AI strategy and architecture, guiding generative AI and ML adoption with governance, security, MLOps, and cross-functional enablement. Advise development teams, drive ROI, and ensure compliant, scalable AI solutions.

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.

Research, design, and implement foundation models for machine-generated and graph data; develop distributed training and inference pipelines; collaborate with engineering and product teams to productionize models; evaluate new AI/ML techniques and drive MLOps, monitoring, and model optimization to improve scalability and reliability.

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.

Design, build, and maintain infrastructure and processes to govern and scale enterprise AI tooling. Integrate data feeds for usage and cost metrics, support LLM lifecycle management and benchmarking, implement AI governance guardrails, collaborate with architecture and cross-functional teams, and create enablement documentation.

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.