Top AI Jobs in Los Angeles, CA

Analyze large datasets using quantitative methods, data mining, statistical modeling, and AI tools to generate actionable product and business insights. The role designs and tracks product metrics, builds dashboards and reports, identifies trends and opportunities, and supports data-driven decisions. Responsibilities include collaborating with product managers, engineers, marketers, and designers, applying statistical techniques such as A/B testing and causal analysis, and independently driving projects to completion.

Designs and develops Azure-based generative AI solutions, including conversational applications, retrieval-augmented generation pipelines, agent workflows, cloud-native microservices, and LLM evaluation systems. The role uses Python, Azure AI Services, Azure OpenAI, LangGraph, vector databases, and DevOps practices to deploy and maintain client solutions. Responsibilities also include managing workstreams, communicating technical tradeoffs, reviewing deliverables, mentoring junior colleagues, and collaborating with clients.

Leads client-facing design and delivery of agentic and generative AI solutions on Azure. Oversees architecture, agent orchestration, RAG pipelines, LLMOps, deployment, testing, security, monitoring, and maintenance. Manages workstreams, budgets, timelines, and deliverables while coordinating with clients and engineering teams. Coaches engineers, reviews technical work, evaluates model behavior, and guides AI-assisted development using Python, C#, and modern AI tools.

Leads client-facing applied AI engineering engagements in healthcare, from solution design through deployment. Translates clinical and health services needs into AI requirements, architectures, and roadmaps; designs RAG, memory, prompt, and agent orchestration components; guides data, model integration, and cloud implementation; evaluates model performance; advises on AI platforms and governance; and develops LLMOps and AgentOps practices. Coaches teams, resolves delivery challenges, and communicates technical decisions to clients and leaders.

Leads client-facing AI adoption and productivity initiatives, designing Azure-based generative AI solutions, prompts, agent workflows, and workforce analytics models. Oversees LLM evaluation, guardrails, governance, deployment, and adoption measurement. Manages engagement workstreams, budgets, timelines, deliverables, client relationships, and team coaching while guiding training and AI-human collaboration programs.

Paid summer internship in NBCUniversal’s Media Tech program, focused on data, AI, and machine learning initiatives. Interns may support content research and measurement, data products, AI and data engineering, SAP data foundations, forecasting analysis, and enterprise AI solutions. The role offers hands-on technical experience, mentorship, networking, and career development in Universal City, California, with a 40-hour weekly commitment.

Lead AI-enabled tools, analytical workflows, and strategic projects for Dynatrace’s Insights team. Partner with leadership, analysts, data science, engineering, and product teams to design, validate, operationalize, and improve AI-driven processes. Interpret complex data, establish governance practices, train teams, document workflows, track adoption and efficiency gains, and research emerging capabilities. The role requires business-focused AI experience, data analysis, executive communication, process design, and the ability to manage ambiguity and iterative innovation.

Leads the design, integration, and delivery of production-grade AI, generative AI, agentic AI, and MLOps solutions for clients. Serves as technical lead, translates requirements into scalable architectures, mentors engineers, manages technical risks and timelines, supports sales proposals, and presents recommendations to stakeholders. Responsibilities include application integration, security, observability, evaluation, model serving, documentation, testing, and operational readiness.

Lead end-to-end performance of customer-facing conversational AI and knowledge base. Design, test, and optimize AI conversations, define intents and success metrics, analyze KPIs, maintain enterprise knowledge, run continuous improvement using analytics and experimentation, and partner cross-functionally to improve CX and business outcomes.

Leads AI and GenAI data science workstreams for client engagements. Develops machine learning, deep learning, and NLP models; applies statistical and predictive modeling; designs scalable data science workflows and pipelines; evaluates model performance; communicates insights through visualizations and executive narratives; coaches teams; and supports responsible AI governance.

Leads strategy, roadmap, investment, and execution for horizontal AI capabilities, shared AI platforms, and AI-native cybersecurity products. Translates AI research into reliable, secure, scalable customer solutions; drives adoption across product teams; establishes launch and autonomy criteria; partners with customers and cross-functional leaders on commercialization; and builds a high-performing AI product organization.

Build secure, production-ready agentic AI applications using AWS and Amazon Bedrock. Develop AI agents, RAG systems, APIs, automations, and enterprise integrations with databases, Jira, Workfront, Oracle, and internal tools. Implement security, access controls, monitoring, testing, governance, and cloud deployment safeguards. Produce maintainable code, document architectures and deployments, evaluate prototypes, and collaborate with AI, security, DevOps, data, and business teams.

Build and deploy production AI/ML systems that optimize Samsara’s global hardware supply chain. Responsibilities include demand, inventory, cost, lead-time, supplier-risk, anomaly-detection, and cash-flow modeling; real-time inference pipelines; MLOps; data infrastructure; governance; and cross-functional AI adoption. The role also sets the supply-chain AI roadmap, mentors junior scientists, establishes modeling standards, and partners with Planning, Procurement, Finance, Engineering, and Operations.

Lead the design and implementation of AI initiatives for edge devices. Develop and optimize machine learning solutions using large-scale sensor, video, text, diagnostic, transaction, and location data. Partner with engineering and business teams to prototype AI experiences, ship production systems, adopt research advances, and improve edge model performance. Mentor ML engineers and communicate technical concepts to varied audiences.

Leads the Safety AI organization and architecture for on-device and cloud-based computer vision and machine learning systems. Oversees sensor fusion, model lifecycle management, platform architecture, experimentation, deployment, monitoring, and continuous improvement across more than two million dashcams. Partners with product, firmware, full-stack, and platform teams to deliver reliable, low-latency safety features. Builds and develops teams of applied scientists and engineers while driving measurable improvements in driver safety and customer outcomes.

Build and deploy agentic and generative AI copilot and autopilot systems for risk and compliance automation. Own end-to-end ML systems, including data integrations, prompt tuning, orchestration, evaluation, productionization, monitoring, and transformer-based model development. Collaborate with product, engineering, platform, and operations teams to improve fraud detection and analyst productivity. Drive strategic roadmaps and cross-functional programs with significant business impact.

Build and deploy generative AI solutions, including APIs, MCP servers, AI agents, LLM integrations, and full-stack applications. Develop scalable AWS cloud services, observability systems, evaluation pipelines, and production agent workflows. Collaborate with Product, UX, Security, Legal, and engineering teams to design interfaces, adapt AI tools to company codebases, and support customer experience and developer productivity use cases.

Build and deploy agentic and generative AI systems that automate risk and compliance investigations. Own full-stack ML systems, including data integrations, prompt tuning, orchestration, evaluation, productionization, monitoring, and transformer-based modeling across transaction, graph, text, and profile data. Collaborate with product, engineering, platform, and operations teams while leading strategic roadmaps and delivering scalable production systems.

Leads the design and deployment of AI-powered agentic workflows and automation across DreamWorks’ animation production pipeline. Responsibilities include architecting AI frameworks, integrating digital content creation tools, managing telemetry, prototyping production solutions, evaluating models and frameworks, building reusable components, monitoring reliability and security, and driving adoption across creative and technical teams. The role also establishes standards, measures productivity impact, and mentors or influences engineers and artists.

Owns the full lifecycle of enterprise AI agent products, from discovering business use cases through deployment and optimization. Partners with engineering, product, program, and business teams to build scalable multi-agent workflows, LLM and RAG solutions, evaluation frameworks, analytics capabilities, and production integrations. Translates ambiguous problems into product requirements, measures adoption and quality, communicates technical concepts, and enables business teams to operate and customize deployed AI solutions.

Leads the design, deployment, and operation of production AI/ML and LLM systems. Responsibilities include building data and ML pipelines, managing model registries and feature stores, developing CI/CD and experimentation frameworks, operating cloud training infrastructure, implementing observability and guardrails, and ensuring security, compliance, reliability, and performance. The role also guides architecture, incident response, governance, and cross-functional collaboration while mentoring engineers and transitioning models from research to production.

Build and deploy applied AI systems across Crunchyroll by embedding with business teams, identifying high-value workflow opportunities, and translating ambiguous problems into reliable technical solutions. Develop agents, RAG systems, automations, integrations, evaluations, observability, guardrails, and human-in-the-loop workflows. Measure business impact, collaborate with technical and business stakeholders, and create reusable components and patterns for scalable AI adoption.

Build and deploy applied AI solutions across Crunchyroll by embedding with business teams, identifying high-value workflow opportunities, and delivering reliable agents, automations, RAG systems, and integrations. Responsibilities include architecture, prototyping, production engineering, evaluations, observability, security, human-in-the-loop controls, stakeholder alignment, adoption, and reusable platform components. The role requires balancing rapid experimentation with scalable, maintainable production systems and measuring business impact after launch.

Leads AI transformation, governance, quality oversight, risk management, and responsible adoption across Pfizer Global Supply and QSEO. Establishes GxP-compliant AI lifecycle practices, inspection-readiness frameworks, governance forums, portfolio monitoring, inventory management, metrics, and reporting. Advises senior leaders on AI regulations, deployment risks, investment priorities, and compliance while leading cross-functional initiatives and influencing stakeholders across Quality, Digital, Operations, and R&D.

Design and implement AI agents, plugins, automations, and enterprise integrations for HR Technology platforms. Lead requirements gathering, technical analysis, solution design, testing, documentation, deployment, security, and data integrity efforts across HR, IT, and business stakeholders. Integrate platforms including ServiceNow, Workday, and other enterprise applications while supporting the HR automation and AI roadmap.