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.

Lead and mature the firm's enterprise AI governance program: maintain AI risk frameworks, assess and challenge AI risks, define controls, partner with cross-functional stakeholders (cybersecurity, privacy, legal), produce executive-level materials, and enable responsible AI adoption while ensuring regulatory and standards alignment.

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.

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 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.

Develop prototypes, PoCs, and MVPs for GenAI solutions. Apply deep learning and ML principles using Python, Hugging Face, LangChain, OpenAI API, TensorFlow/Keras/PyTorch, and cloud model services. Work with multi-modal data and intelligent agent tools. Be self-motivated, collaborative, and focused on solving hard AI/GenAI problems.

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.

Lead design, development, and production deployment of scalable ML systems across domains. Own end-to-end ML lifecycle, architect pipelines and serving, drive MLOps maturity, mentor engineers, conduct design/code reviews, and ensure monitoring, governance, and model reliability in production.

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.

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.