Artificial Intelligence Jobs in Thousand Oaks, CA

Design and develop scalable software systems and production LLM solutions using RAG, agentic workflows, PEFT, and guardrails. Own architecture, technical execution, SDLC activities, cloud and Databricks governance, DevOps automation, testing, deployment, and system reliability. Collaborate with cross-functional teams, conduct code reviews, translate business requirements, and guide software design. The role is client-facing and follows a hybrid work model with travel as needed.

Own and grow strategic enterprise Life Sciences accounts, build executive relationships, develop account and growth strategies, manage P&L and delivery quality, expand analytics, AI, and cloud platform opportunities, and shape proposals and executive content to win large engagements. Partner with delivery and product teams to deliver strong client outcomes and translate advanced technology into commercial business impact.

The AI Engineering Specialist will oversee project tasks, manage expectations, mentor junior members, and design scalable AI systems while working with advanced AI models and ML pipelines.

Leads pharmaceutical sales planning, analytics, field-force effectiveness, territory optimization, incentive compensation, performance reporting, and dashboard development. Analyzes complex commercial data to identify opportunities, support targeting and account planning, measure marketing effectiveness, and improve sales execution. Coordinates cross-functional projects, data quality, user testing, training, and continuous improvement initiatives, including AI applications. The role also manages stakeholders and presents insights to senior leadership.

In this role, you will design and deploy advanced machine learning and Generative AI solutions, leading projects from conception to deployment while mentoring junior team members.

The Enterprise Architect will design scalable AI solutions, mentor teams, support business development, and manage client relationships while evolving technology practices.

Ph.D. interns apply machine learning and AI to semiconductor chip design or enterprise engineering tools. Responsibilities include developing ML models for EDA automation, placement, timing, power, and verification, or deploying LLM, RAG, and agentic systems. Interns will train, evaluate, and deploy models, conduct rigorous experiments, collaborate with engineering and technology stakeholders, assess production performance and safety, and present findings to leadership. The role requires doctoral research in AI, machine learning, computer science, electrical engineering, data science, or a related field.