Top AI Engineer Jobs in Los Angeles, CA

Build and operate secure, scalable AI infrastructure and cloud-native platforms across on-premises and cloud environments. Develop AI agents, MCP servers, microservices, automation, and Kubernetes-based workloads. Establish governance, security, observability, and operational standards for AI systems. Monitor infrastructure, troubleshoot production issues, improve processes, document runbooks, and support platform reliability while collaborating with artists, production teams, and engineers.

Develop and implement AI solutions and machine learning systems that enhance products and solve complex client problems. Responsibilities include advanced analytics, LLM production deployment, prompt engineering, RAG pipeline optimization, cross-functional collaboration, technical discovery, responsible AI advocacy, client relationship building, and mentoring junior team members.

Design, build, deploy, and maintain resilient, scalable backend services and platform components for Toast's Voice AI platform. Lead cross-cutting architecture and projects, integrate AI platforms (LLMs), implement observability and evaluation for AI systems, set engineering standards, and mentor engineers.

Own the reliability and quality of an AI copilot for a trading platform. Build evaluation frameworks, benchmarks, quality gates, monitoring, incident response, and model improvement workflows. Partner with engineering and product to create safe, auditable tool interactions and improve assistant behavior. Develop domain expertise in trading, risk, margin, execution, and portfolio reasoning while operating across model, backend, and frontend systems.

Own production operations for workplace AI platforms, including configuration, integrations, observability, secrets, access controls, model and prompt changes, benchmarking, and incident response. Translate technical findings into governance, risk, compliance, and training materials while collaborating with Security, Legal, HR, and business stakeholders. Investigate failures, implement mitigations, document root causes, and maintain reliable, secure AI services.

Lead design and delivery of advanced data and ML solutions, performing exploratory analysis, statistical modeling, and visualization to inform strategy. Manage and coach teams, collaborate with clients to validate outcomes, and improve analytics processes and data quality to drive business decisions.

Lead and coach teams to design, develop, and deliver production-grade AI and data solutions for clients. Manage client accounts, ensure quality and timelines, analyze complex datasets, implement best practices, and drive continuous improvement while fostering client relationships and technical excellence.

Supports AI engineering initiatives at Vantor, contributing to spatial intelligence solutions and helping develop technologies that enable customers to understand current and future conditions. The posting provides limited role-specific responsibilities but emphasizes problem-solving, innovation, collaboration, and mission-focused work. Applicants must qualify as a U.S. Person under applicable export control and ITAR requirements.

Develop and operate production ML and AI systems powering patient experiences such as matching, ranking, recommendations, onboarding, and personalization. Build reusable ML infrastructure, pipelines, evaluation, observability, and model-serving capabilities. Provide technical leadership across the ML lifecycle, guide architecture, evaluate custom versus foundation-model approaches, and partner with ML and Patient Engineering teams to deliver reliable, scalable AI products.

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.

Build and manage AI-powered workflows, agents, automations, and internal applications for Deal Desk and GTM teams. Identify automation opportunities, develop prototypes, scale solutions to production, integrate enterprise data and APIs, implement human validation and audit controls, optimize LLM costs, document tools, measure adoption, and train users. The role requires Deal Desk or RevOps experience plus practical knowledge of AI models, APIs, scripting, SQL/SOQL, Python, CPQ, CLM, Salesforce, and agentic platforms.

Develop, train, and evaluate machine learning models for space-domain applications including object classification and anomaly detection. Build data ingestion, preprocessing, feature engineering, and model evaluation pipelines; conduct experiments; track results; and write clean, testable Python code. Collaborate with senior engineers while contributing to AI capabilities supporting space security and autonomous systems.

Design, develop, deploy, and support AI solutions including generative AI applications, agents, workflow automation, document intelligence, and retrieval-augmented generation. Partner with business and IT teams to identify use cases, integrate enterprise platforms and data sources, establish prompt and evaluation standards, support AI governance and security, monitor solution performance, and train employees on responsible AI adoption.

Build AI-powered workflows, agents, integrations, and internal tools that automate People Operations. Partner with People, Engineering, and IT to modernize onboarding and other employee processes, while implementing secure access controls, human oversight, escalation paths, and production-ready architectures. Prototype with users, iterate quickly, and measure improvements in efficiency, employee experience, and operational consistency.

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.

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.

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.

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.

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.

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.

Develop AI communication systems for humanoid robots, including generative AI, NLP, speech-to-text, text-to-speech, computer vision, multimodal interaction, gesture recognition, and emotion-aware capabilities. Optimize transformer-based models for real-time edge execution and integrate speech, vision, and movement for natural human-robot interactions.

Lead the architecture, build, and deployment of AI-driven automation and intelligent systems to solve security, compliance, and risk challenges. Develop real-time intelligence, workflow automation, and risk-based decision-making tools, and engage customers and partners to refine and deploy production-grade solutions.