Top AI Jobs in Los Angeles, CA

Design, build, and maintain optimization technology, full-stack systems, and data-intensive scheduling infrastructure. Improve optimization algorithms using linear optimization, machine learning, and generative AI. Lead complex technical initiatives, establish testing and observability, support scalable operations, troubleshoot customer issues, participate in on-call rotations, and mentor engineers while partnering closely with product and engineering teams.

Architects and develops AI-powered systems, tools, agents, backend services, APIs, and experimentation frameworks using foundational AI models. Integrates AI capabilities with microservices, AWS, Snowflake, and complex data systems while ensuring scalability, reliability, and maintainability. Translates business problems into technical solutions, prototypes AI applications, collaborates with product and engineering teams, evaluates emerging AI technologies, and documents system designs and integration patterns.

Lead teams building computer vision and machine learning for AI dash cameras, managing model lifecycle, coordinating with cross-functional teams, and driving AI product development.

Build and ship production AI systems across retrieval, knowledge graphs, inference, agentic workflows, and data platforms. Develop customer-context retrieval, scored signals, evaluation and observability capabilities, and workflows that turn insights into action. Work hands-on with large and small models, durable orchestration, cloud deployment, inference-cost optimization, and high-volume observability data. Establish reusable engineering patterns, collaborate with product and platform teams, and mentor through strong technical contributions.

Develop and deploy machine learning systems for Mars missions, enhance atmospheric modeling, and integrate multi-modal data for autonomous science.

The role involves developing machine learning systems for Mars atmospheric modeling and multi-modal data fusion, contributing to autonomous science in space missions.

Design, build, and operate agentic AI systems end-to-end for Professional Services. Translate stakeholder needs into secure, scalable multi-agent architectures, define performance benchmarks, manage deployments and incident response, advise leadership, mentor teammates, and maintain runbooks and production tooling including on-call responsibilities.

Build and productionize agentic workflows, RAG pipelines, AI services, backend systems, and ML pipelines for sales automation. Integrate Salesforce, quoting engines, data lakes, and internal platforms using scalable, event-driven architectures. Lead 0-to-1 development of tools for account research, lead prioritization, and quoting while ensuring reliability, observability, performance, and resilience across the GTM technology stack.

General application to match applicants with Launch Institute of Technology training programs in AI, clean energy, and advanced technology. Collects background, interests, and location to determine program eligibility and availability (remote/hybrid varies by state).

Develops and maintains prompts, responses, guidelines, and datasets for generative AI and large language model tools. Leads labeling initiatives with internal teams and third-party firms, trains stakeholders on LLM and dataset best practices, and supports quality assurance and testing. Requires native-level English, bilingual ability in a specified language, specialized domain knowledge, and a college degree or equivalent experience.

Design, build, and operationalize scalable AI and machine learning solutions for enterprise clients. Responsibilities include selecting and customizing deep learning and generative AI models, developing data and MLOps/LLMOps pipelines, managing large datasets, implementing model storage and retrieval, conducting AI research, and integrating production systems. The role requires collaboration with technical and business stakeholders, client-facing consulting skills, and travel that may range from none to 100%.

Designs and deploys agentic commerce and generative AI solutions, including multi-agent architectures, RAG systems, data and knowledge pipelines, orchestration services, and cloud-native deployments. Integrates AI with commerce platforms, APIs, vector databases, and enterprise tools while applying responsible AI, monitoring, testing, and optimization practices. Advises clients, translates technical solutions into business value, creates reusable accelerators, presents solutions, and mentors junior team members.

Designs and implements enterprise-grade AI architectures, platforms, and solutions across public, private, and sovereign clouds. Leads architecture workshops, evaluates technologies, creates AI security, governance, observability, AgentOps, LLMOps, infrastructure, and knowledge-layer blueprints, and builds reusable AI agents and components. Optimizes AI systems for performance, cost, reliability, and trust while delivering proofs of concept and production-ready solutions. The role requires extensive experience with scaled agentic AI, generative AI, and large-scale cloud-based AI/ML systems.

Design and implement enterprise AI transformation solutions using agentic architectures, multi-agent orchestration, RAG, vector and graph databases, event-driven systems, and cloud platforms. Analyze legacy systems, identify dependencies and constraints, and create composable AI-native architectures that support human-AI collaboration. Explain complex AI concepts to nontechnical audiences, build production-ready systems, and apply observability, debugging, and fallback strategies. The role involves hands-on consulting and may require extensive client travel.

Design and lead AI-native enterprise transformations using multi-agent systems, LLMs, composable architectures, cloud platforms, microservices, event-driven systems, and legacy-system modernization. Explain AI capabilities and business value to executives, lead agile teams, design AI infrastructure and DevOps practices, and address model deployment, monitoring, prompt versioning, cost, and latency. The role is hands-on and client-facing, with travel varying from 0% to 100%.

Senior technical leader responsible for architecting and building cloud-native, microservices-based platforms with AI capabilities. Lead design and delivery of scalable search and AI solutions (LLMs, RAG), data pipelines, search optimization (Elastic/OpenSearch/Solr), CI/CD and DevOps adoption, observability, and mentor engineering teams while driving responsible AI practices and stakeholder collaboration.

Build and deploy agentic AI systems, MCP server infrastructure, retrieval pipelines, and evaluation frameworks for an Oracle enterprise knowledge platform. Support AI-assisted sales pursuits through generated content, live demo agents, and presentation assets. Serve as an Anthropic and AI coding agent subject matter expert while partnering with architects, sales, delivery, and client teams to embed AI across the Oracle-Accenture alliance.

Leads the architecture, development, and operation of Disney’s AI and machine learning platform for finance. Responsibilities include managing a global engineering team, building production LLM applications and governed data foundations, defining AI/ML roadmaps, enforcing RBAC and engineering quality, guiding build-versus-integrate decisions, partnering with finance stakeholders, and driving enterprise adoption. The role is hands-on, involving architecture, implementation, technical reviews, code review, model evaluation, and delivery oversight.

Leads AI presales strategy, customer advisory, technical solution architecture, and team development. Translates business needs into AI use cases, prototypes, business cases, roadmaps, and repeatable offerings. Partners with sales, engineering, consultants, and technology vendors to support complex pursuits, executive briefings, demonstrations, proofs of concept, and AI business growth. Promotes responsible AI governance, tracks pipeline performance, develops thought leadership, and remains hands-on in high-impact customer engagements.

Leads AI enablement and legal engineering across the legal organization by designing AI-powered workflows, document-processing pipelines, automation, and knowledge systems. Evaluates legal technology vendors and build-versus-buy decisions, measures adoption and ROI, establishes quality controls, and develops AI fluency across legal teams. Partners with senior legal, technology, finance, and HR leaders to modernize legal service delivery and translate technical capabilities into practical business solutions.

Develop and maintain AI-powered applications, agents, automations, and integrations for legal and operational workflows. Build LLM tools, RAG systems, APIs, and workflow automations using Python and/or JavaScript/TypeScript. Manage projects from prototype through deployment, evaluate AI performance, maintain secure integrations, and collaborate with attorneys, leadership, and technical teams to improve internal processes.

Design and implement AI architectures and solutions for client business needs. Develop innovative solutions using generative and agentic AI, deep learning, computer vision, NLP, and related technologies. Apply software engineering expertise, work independently, mentor team members, and potentially lead Agile teams. The role requires a bachelor’s degree, Secret clearance eligibility, and at least two years of relevant experience.

Review, label, and QA datasets for AI/ML training; maintain trackers, databases, and dashboards; follow annotation guidelines and SOPs; prepare and clean data; document processes; collaborate to resolve data queries while ensuring confidentiality and timely delivery.

Leads the firm's AI strategy and implementation initiatives by identifying automation opportunities, building AI-powered workflows and agents, integrating AI tools with existing systems, and optimizing solutions across legal and business functions. The role also trains employees, develops documentation and SOPs, manages projects through implementation, and drives organization-wide adoption of AI technologies.

Design, prototype, deploy, and scale production AI workflows, agents, copilots, and automations for enterprise clients. Integrate foundation models with platforms such as SAP, ServiceNow, Salesforce, and internal systems. Translate ambiguous business needs into technical solutions, lead architecture and API orchestration, bridge product and engineering stakeholders, and ensure secure, reliable, governed AI deployments. The role is client-facing and may require 25% to 75% travel.