Top Artificial Intelligence Jobs in Scottsdale, AZ

Validate and challenge ML/NLP/Generative and Agentic AI models across the model lifecycle: review data and sampling, replicate models, develop tests and monitoring, build reusable validation code, write analytical reports, coach junior staff, and communicate with stakeholders, regulators, and auditors.

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.

Lead identification, assessment, and execution planning for AI/ML, automation, and process improvement initiatives. Perform process mapping, data analysis, SME interviews, KPI definition, and change management. Partner with business, finance, and risk stakeholders, prepare senior-management presentations, and support adoption and governance of scalable solutions.

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.

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.

Design, build, and deploy ML models and end-to-end pipelines; implement MLOps practices; deploy containers to cloud production; monitor model performance and drift; collaborate with engineers and product teams; optimize latency and throughput; write production-quality code and mentor junior engineers.

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.

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.

Own operational readiness and productionization for internal AI-enabled tools: build and maintain CI/CD and IaC, support Vercel and multicloud deployments, improve monitoring, secrets and access controls, runbooks, and lifecycle standards. Partner with Corporate AI, IT, Security, and business teams to move prototypes to production and ensure reliability, security, and maintainability.

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.

Senior full-stack AI platform engineer to design, build, and operate MCP servers, AI-enabled applications, AWS serverless infrastructure, APIs, and secure integrations. Lead architecture, implement security and governance, develop reusable SDKs/patterns, and support responsible AI adoption across the university.

Work directly with customers to configure and deploy AI Employees using Nextiva's platform. Translate business requirements into prompts, deterministic conversation flows, routing logic, workflows, and simple API-enabled tools. Test, document, train customers, and share feedback with product and engineering while learning prompt engineering and AI implementation practices under mentorship.

Design, build, and deploy enterprise generative AI solutions using LLMs, RAG, vector search, OCR, and AI agents. Develop cloud-native, Kubernetes-based services, REST APIs, and integrations; contribute to CI/CD, IaC, and architecture reviews; mentor team members and ensure security, observability, and software quality.

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.

Lead a US AI implementation team and run customer deployments: translate requirements into scalable LLM-powered workflows, configure prompts, conversational flows, integrations and automations, test and iterate, train customers, manage escalations, and build implementation processes and best practices to scale delivery.

Lead development, validation, and deployment of AI/ML tools for histopathology and multimodal clinical data. Manage end-to-end projects including data curation, modeling, evaluation, regulatory considerations, and clinical integration. Collaborate with pathologists, informatics, IT, and clinicians to build secure MLOps pipelines, research translational applications, and develop educational AI tools.

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.

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.