Artificial Intelligence Jobs in Santa Ana, CA

The AI AppSec Engineer V will secure AI solutions, mentor teams, and ensure the integration of security into AI development lifecycles. Responsibilities include threat modeling, code analysis, and contributing to security policies.

Build and maintain marketing data pipelines, replace spreadsheet workflows with structured databases, automate data cleaning/deduplication and syncing, develop AI-driven reports and dashboards, and collaborate with marketing, AdOps, and engineering to ensure reliable, unified, AI-ready marketing data.

Lead design, implementation, optimization, and administration of scalable database systems and AI/ML data infrastructure. Build vector DBs, embedding stores, RAG architectures, ETL pipelines, and integrations with LLM APIs. Provide technical leadership, performance tuning, security, data governance, and support for AI-enabled applications while mentoring junior staff and driving data quality and monitoring.

Lead end-to-end design, build, and operation of production generative-AI systems (copilots, RAG, agents). Partner with business stakeholders, implement retrieval pipelines, evaluation harnesses, security/privacy controls, observability, and cost optimization. Codify reusable patterns, runbooks, and ensure compliance and operational excellence for deployed AI solutions.

Early-career role to build, operate, and improve cloud data/analytics platforms (Fabric, Snowflake, Databricks). Support platform operations, CI/CD for analytics assets, onboarding for BI tools (Power BI/Tableau), runbooks, monitoring, incident response, and basic security/governance. Work under senior mentorship and contribute to automation, deployment templates, and performance triage.

Lead architecture, operation, and improvement of cloud data and analytics platforms (Fabric, Snowflake, Databricks). Own CI/CD for data assets, BI delivery (Power BI/Tableau), reliability, monitoring, incident response, security, and governance. Enable teams through standards, runbooks, and onboarding; troubleshoot performance and ensure production stability.

Lead global product marketing for AI systems and accessories, set positioning and go-to-market strategy, manage a team of product marketing managers, translate technical AI hardware capabilities into customer value, drive launches, partnerships, competitive intelligence, and commercial performance across consumer and professional segments.

Design, build, deploy, and maintain AI-integrated full-stack web applications, chatbots, and dashboards. Collaborate with ML and backend teams to ensure secure, scalable, high-availability production systems and monitor model outputs and training analytics.

Develop and operate Digital Twin infrastructure and AI-assisted systems engineering tools. Build multi-fidelity FMU plant models and FMI co-simulations, deploy LLM-based pipelines (RAG, semantic search) integrated with GitLab CI/CD, and perform hands-on systems engineering, requirements authoring, safety analyses, and model correlation with vehicle and fleet data.

The Lead Product Engineer will design and deploy enterprise applications, drive Azure migrations, and collaborate with cross-functional teams in an AI-driven environment, mentoring junior engineers and implementing best practices.

Lead GenAI strategy and research to develop scalable LLM and agentic AI applications (e.g., conversational assistants). Analyze data to personalize investment outcomes, collaborate with engineers, productionize models, ensure best practices in data management and evaluation, publish research, and mentor applied scientists.

Lead design and delivery of enterprise-scale AI/GenAI platforms and Databricks architectures across major cloud providers. Drive technical strategy, client discovery, reference architectures (data pipelines, feature stores, model training, vector DBs, real-time inference), mentor teams, define responsible-AI and security best practices, and represent the firm externally.

Own product marketing strategy and execution for AI laptops/desktops/workstations. Develop positioning, messaging, launch and go-to-market plans; translate technical AI/NPU capabilities into customer value; create sales and communications assets; coordinate regional localization; track product performance; support PR, events, and cross-functional stakeholders throughout the product lifecycle.