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

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, optimize, and deploy ML models for resource-constrained edge devices. Apply quantization, pruning, and hardware-aware optimizations. Build and tune cross-platform inference runtimes, implement secure on-device workflows, design hybrid edge-cloud architectures, and create telemetry and benchmarking suites. Collaborate with hardware, firmware, and product teams and maintain documentation and responsible AI practices.

Design, build, and operate petabyte-scale AI data pipelines for training and evaluation across multimodal data. Implement ingestion, cleaning, lineage, versioning, high-throughput loading, labeling/active-learning workflows, privacy controls, observability, and cost/performance optimizations while collaborating with ML researchers and documenting systems.

Lead prompt engineering and LLM application architecture: define standards, design prompts and agentic systems, build RAG pipelines, evaluation frameworks, tooling, safety guardrails, mentor teams, and optimize latency/cost/quality tradeoffs.

Design, train, and deploy reinforcement learning systems for decision-making problems. Build and evaluate policies at scale, handle reward modeling and simulation, train neural policies on GPU clusters, and transition RL solutions from research to production with safety and stability focus.

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 a team defining strategy and roadmap for an intelligence platform that surfaces prioritized signals and insights. Partner with data science and engineering to translate models into product experiences, drive 0-to-1 product development, align cross-functional stakeholders, manage full product lifecycle, and improve user experiences through qualitative and quantitative analysis.

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.

Senior enterprise technology leader to define and execute ASUs AI-ready data ecosystem, enterprise integration, and intelligent platform architecture. Lead AI/ML platform strategy, semantic analytics, cloud-native infrastructure, responsible AI governance, and organizational transformation to scale AI-enabled student success, research acceleration, and institutional operations.

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.

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.

Build internal AI-powered applications for Solutions Engineering, Sales, and Marketing: prototype with AI-native tools, integrate APIs and cloud services, deploy and maintain production products, and drive adoption and continuous improvement.

Lead AI/ML product management for strategic, cross-divisional products. Define features, implementation plans, and market analysis; drive CI/CD, automated deployment, and UX improvements. Coach technical teams, influence stakeholders, and align business outcomes to product strategy.

Build, deploy, and maintain internal AI-powered applications and orchestration pipelines. Re-engineer business workflows for automation, define standards for reliability and observability, collaborate with IT/Security/DevOps, and mentor team members to drive company-wide AI adoption and measurable efficiency gains.

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.

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.

Lead an AI Agents and Automation product area: define strategy, build prototypes using agentic AI/LLMs, translate technical requirements, partner cross-functionally, monitor metrics, manage workstreams, and ensure product profitability and regulatory compliance in payments.

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.