Artificial Intelligence Jobs in Irvine, CA

Own lifecycle and performance of AI products across voice, SMS, and agent-assist tools. Define KPIs, run performance reviews and POCs, write PRDs, author and evaluate prompts, build QA frameworks, run vendor evaluations, write production SQL for analysis, partner with engineering/data for pipelines, and ensure AI governance and compliance.

Serve as senior technical lead for enterprise AI initiatives, building and maintaining Copilot Studio agents, Power Platform automations, Azure AI integrations, and governance. Translate business needs into scalable solutions, guide citizen development, support M365 Copilot pilots, document architectures and runbooks, and complete structured knowledge transfer at contract end.

The AI/Machine Learning Engineer II will design and develop models to analyze data for health monitoring devices, focusing on diagnosis and prediction using various algorithms.

The Sr Engineer, AI/Machine Learning will design and develop AI and ML models for health monitoring devices, analyzing data and collaborating across teams to enhance product capabilities.

The VP, Algorithm - AI leads algorithm strategy, development, and execution within Masimo, focusing on AI, machine learning, and signal processing to drive innovation and clinical impact.

The Sr Engineer will develop AI/ML models for health monitoring devices, focusing on algorithm development, data analysis, and cross-functional collaboration.

As an AI/ML Engineer, you will develop multimodal AI models, focusing on computer vision and agentic capabilities. Responsibilities include data curation, model training, evaluation, and deployment while working with real robotic systems.

The role involves designing AI systems for IC calibration and optimization, collaborating with design teams, and applying ML techniques to analog/mixed-signal circuits.

Lead a global GTM operations team to redesign, automate, and integrate GTM processes with an AI-first approach. Identify, prioritize, and deploy AI/automation solutions and integrations at scale, collaborate with IT and cross-functional leaders, drive adoption and change management, and serve as a trusted advisor to GTM leadership.

Design, prototype, and deploy agentic AI solutions and AI Ops infrastructure (orchestration, memory, tooling, observability). Build RAG/retrieval systems, evaluation and testing pipelines, and agent-native workflows to automate engineering and customer-facing tasks. Collaborate with engineering, product, and field teams to scale reliable, field-ready AI for robotics deployments.

Lead end-to-end silicon diagnostic pipeline using AI/ML to analyze scan failures and process data, build models for failure classification and predictive yield, orchestrate AI-based EDA tools, and drive cross-functional root-cause analysis to improve yield and time-to-market.

The Pre-Sales Data and AI Architect will design and operationalize enterprise data architectures, collaborating with teams to ensure effective integration of Salesforce D360 into complex ecosystems while driving customer success through innovative data solutions.

Lead architecture and implementation of production-grade agentic systems integrating LLMs across applications. Drive LLM integration strategy, cognitive automation roadmap, scalable distributed system design, best practices, cross-functional alignment, and mentor engineering talent.

Design, build, and operate production AI infrastructure: architect reusable IaC (Terraform/Crossplane), implement GitOps with FluxCD, create advanced ADO pipelines, resolve complex AKS issues, implement workload identity, define observability, perform technical reviews, and mentor junior engineers.

Hands-on AI Architect to design and build production-ready LLM and agent-based AI solutions. Responsibilities include developing Python backends, retrieval-augmented (RAG) systems with vector DBs and embeddings, API and cloud-native (AWS) integrations, CI/CD, observability, and deploying ML models while guiding enterprise AI governance and workflows.

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 firmwide AI enablement by setting strategy and roadmap, designing multi-level training, running an AI champion community, producing adoption analytics, shaping governance, evaluating tools and vendors, and managing stakeholders and a small team to drive responsible AI adoption and measurable business impact.

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.

Lead cross-functional teams to deliver business strategy and AI advisory services, driving strategic client relationships and AI adoption.

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.

Design, develop, and deploy AI-powered agents and RAG solutions. Build agentic workflows, integrate LLMs, create data pipelines and embeddings, implement APIs and microservices, deploy on cloud, and monitor scalable AI applications. Collaborate across product and engineering to deliver production-ready solutions.

Design and build Executive Communication Agents for leadership operations, focusing on automation of workflows and integrations with enterprise tools while collaborating closely with executives.

As Agentic AI Architect, you'll design AI agent ecosystems, lead architectural development, mentor engineers, and ensure solutions meet business challenges in various domains.

Design and implement AI solutions for automotive applications, focusing on LLM, RAG systems, and full-stack AI workflows while collaborating across teams.

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.