Top AI Engineer Jobs in Los Angeles, CA

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 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.

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.

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.

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.

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.

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.

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.

Set the technical direction for Rapid7’s LLM and agentic AI systems. Lead architecture, model strategy, fine-tuning, post-training, inference optimization, evaluation standards, and complex cross-team initiatives. Build critical model and inference code, mentor senior engineers, resolve technical trade-offs, and influence product and platform strategy across cybersecurity products.

Sets Rapid7’s company-wide AI technology vision, architecture strategy, and multi-year roadmap for security products. Leads production LLM and agentic AI initiatives, establishes engineering, evaluation, and responsible-AI standards, and guides model and infrastructure investments. Partners with executives, mentors senior engineering leaders, solves high-impact technical challenges, and represents Rapid7 externally. The role spans LLMs, predictive AI, classical machine learning, distributed systems, and scalable AI production infrastructure.

Designs, builds, and ships production LLM and agentic AI systems for cybersecurity products. Leads architecture, model strategy, fine-tuning, post-training, inference optimization, evaluation, and technical roadmaps. Establishes engineering practices, mentors engineers, leads design reviews, prototypes critical model and training code, and partners cross-functionally to deliver scalable, cost-effective AI solutions.

Build and operate Affirm’s AI platform, including LLM and MCP gateways, agent runtimes, evaluation services, and governance controls. Design highly available backend systems, manage identity, quotas, routing, caching, reliability, observability, and security for AI workloads. Own projects from design through launch and on-call, establish engineering standards, collaborate across teams, mentor engineers, and help scale AI systems into production.

Build and deploy production-grade generative AI and agentic systems for enterprise clients. Responsibilities include identifying high-value business problems, shaping architecture, developing multi-agent workflows, APIs, data pipelines, integrations, and distributed systems, and owning solutions from discovery through deployment. The role requires hands-on ownership of evaluation, observability, security, reliability, scalability, latency, and cost, along with direct collaboration and communication with senior client leaders.

Designs, develops, and supports digital tools, automated workflows, dashboards, data integrations, and AI-enabled applications for legal services and law firm operations. Responsibilities include requirements gathering, solution delivery, Power Platform development, AI agent configuration, testing, documentation, project management, stakeholder engagement, training, adoption, and responsible evaluation of emerging technologies. The role requires collaboration with attorneys, business teams, data specialists, vendors, and governance stakeholders while protecting confidential and client information.

Deploys and integrates Circadia’s AI platform with healthcare customers, tailoring predictive and LLM-based workflows to clinical and operational needs. Builds EHR, care-coordination, billing, and compliance integrations; troubleshoots production and data-quality issues; collaborates with clinical stakeholders and internal teams; and develops reusable onboarding tools, documentation, and deployment playbooks.

Designs and deploys AI-powered features and scalable AI/ML services across SaaS products. Responsibilities include translating business problems into AI solutions, integrating large language models, preparing data, evaluating model performance, writing Python, building secure deployment infrastructure, and collaborating with product, data, and cloud operations teams. The role requires production AI/ML experience, scalable data infrastructure expertise, AWS familiarity, and modern DevOps practices.

Design, build, deploy, and operate production AI-native systems spanning applications, APIs, data, cloud infrastructure, DevSecOps, security, observability, and agentic AI. Lead architecture, client engagements, multidisciplinary engineering teams, and technical delivery from ambiguous requirements through production. Build cloud-native platforms, CI/CD pipelines, secure data and context architectures, AI integrations, agent orchestration, evaluations, and production safeguards. Establish reusable engineering standards, tooling, and practices for enterprise AI adoption.

Lead the strategy, architecture, deployment, and operation of League of Legends’ AI/ML platforms at global scale. Establish MLOps, serving, feature platform, governance, security, observability, incident response, and cost optimization standards. Drive implementation across multiple products, improve ML developer experience, mentor senior engineers, coach cross-functional teams, and influence recruiting and product strategy.

Build and deploy AI-powered tools for Rula’s business and clinical teams by observing workflows, rapidly delivering production software, measuring usage and impact, and enabling teams to operate tools independently. The role requires privacy-by-design practices, rigorous AI evaluation, customer-centric discovery, and integration with complex legacy systems. Staff engineers may optimize LLM costs and quality while creating reusable building blocks for future teams.

Own and ship backend systems and agentic AI features for telemetry analysis: design agent tool interfaces, build distributed job execution on Kubernetes, implement APIs and MCP server, integrate frontier models, and build evaluation and observability for production agent workloads.

Own the design, roadmap, security, observability, and production operations of Tinder’s internal AI agent platform. Build gateways, MCP connectors, identity and delegation systems, fine-grained authorization, guardrails, audit trails, evaluation workflows, cost attribution, and reliable runtime infrastructure. Partner with Security and internal engineering teams, lead incident response and on-call operations, establish connector standards, and mentor engineers building on the platform.