Fulltime Reinforcement Learning Phd Jobs in Nevada

The Vice President of Artificial Intelligence (VP of AI) will lead enterprise-wide AI strategy, innovation, and execution. This role oversees the development, deployment, and governance of AI/ML systems across the organization, ensuring measurable business value, responsible AI practices, and alignment with corporate strategic goals. The VP of AI collaborates closely with Technology, Data, Operations, Risk, Compliance, and Business Unit leadership to build scalable AI platforms, optimize business processes, and accelerate digital transformation.

• Define and lead the enterprise AI strategy, including advanced analytics, machine learning, deep learning, and generative AI capabilities.
• Build and oversee AI Centers of Excellence (CoE) to drive innovation, reusable solutions, and best practices.
• Partner with IT, Data Engineering, and Cloud teams to establish a scalable AI/ML platform and MLOps frameworks.
• Identify high-impact AI opportunities that drive automation, operational efficiency, customer experience improvements, and revenue growth.
• Establish standards for Responsible AI, model governance, explainability, bias detection/mitigation, and regulatory compliance.
• Lead the development, deployment, and lifecycle management of AI/ML models across multiple business units.
• Oversee the creation of reusable AI components, annotation processes, model training pipelines, and evaluation frameworks.
• Implement enterprise-wide generative AI solutions including LLMs, copilots, prompt engineering frameworks, and knowledge automation tools.
• Collaborate with cybersecurity leaders to implement secure AI architectures, data protection controls, and model threat-defense mechanisms.
• Promote cross-functional collaboration through transparency, communication, and evangelism of AI capabilities.
• Build and manage high-performing AI teams including machine learning engineers, data scientists, AI product managers, and researchers.
• Support annual planning, budgeting, strategic roadmaps, and executive-level presentations for AI programs.
• Continuously monitor emerging AI trends, tools, and technologies and recommend adoption as appropriate.
• Perform other duties as assigned.

• Bachelor’s degree in computer science, Engineering, Data Science, or related field. Master’s or PhD preferred.
• 12–15+ years of progressive experience in AI/ML, software engineering, or data science, with 7+ years in leadership roles.
• Demonstrated experience architecting, deploying, and scaling machine learning or deep learning systems in production.
• Deep knowledge of Responsible AI frameworks, risk controls, and regulatory expectations.
• Strong experience with cloud platforms (Azure preferred), distributed systems, and MLOps.
• Exceptional communication skills, with ability to translate complex AI concepts for senior executives.
• Proven ability to lead and inspire diverse technical teams.
• Ability to drive outcomes, influence strategic decisions, and deliver business value.
• Demonstrated alignment with company values of excellence, ownership, collaboration, and integrity.

• Supervised, unsupervised, reinforcement learning
• Deep learning (CNNs, RNNs, Transformers)
• Natural Language Processing (NLP) & LLMs
• Generative AI (diffusion models, fine-tuning, RAG)

• Model training, deployment, monitoring, and retraining
• Feature stores, vector databases, and model registries
• CI/CD pipelines for ML (MLOps)
• GPU/accelerator compute architectures

• Azure AI, Azure ML, AWS Sagemaker, or Google Vertex AI
• Kubernetes, containerization, microservices
• Data platforms (Databricks, Snowflake, Synapse)

• Model explainability (SHAP, LIME)
• Fairness, bias detection, model risk controls
• Privacy-preserving ML techniques (differential privacy, federated learning)

• Python, PyTorch, TensorFlow, JAX
• LangChain, semantic search, vector embeddings
• Prompt engineering & LLM orchestration frameworks