Top AI Engineer Jobs in Los Angeles, CA

Designs and builds large-scale clinical, claims, and document data platforms on Google Cloud. Leads architecture, production engineering, AI-assisted development and operations, including agent guardrails, observability, automation, auditability, and regulated-environment controls. Develops AI and machine learning systems involving retrieval, extraction, evaluation, and serving. Partners across engineering, security, privacy, compliance, and governance teams while communicating technical strategies to executives and customers.

Support and scale cloud, DevOps, and production ML infrastructure. Responsibilities include managing AWS, Databricks, SageMaker, Kubernetes, CI/CD pipelines, Infrastructure as Code, model serving, monitoring, security, observability, incident response, and disaster recovery. The role automates deployments and operational processes, improves reliability, supports AI and agentic products, and collaborates with software, ML, QA, security, and support teams.

Build and deploy production AI, generative AI, machine learning, data engineering, and advanced analytics solutions for a regulated bank. Develop Snowflake-centered pipelines and Azure integrations, implement LLM, RAG, agent, and automation capabilities, and establish testing, monitoring, documentation, governance, security, and responsible AI practices. Partner with business, technology, risk, compliance, and legal teams to identify valuable use cases, evaluate emerging technologies, and scale reliable AI capabilities.

Design, implement, and train reinforcement learning and multi-agent algorithms; build scalable Python-based training pipelines and simulated environments; run distributed training with cloud and container orchestration; transition models to production, optimize system performance, and collaborate with domain experts to align rewards and evaluations with mission objectives.

Lead the technical direction, architecture, and development of Compass, an LLM-powered agent platform. Design multi-agent systems, MCP and A2A integrations, orchestration, evaluation, and observability infrastructure. Drive cross-team technical projects, production support, engineering quality, mentoring, and adoption of AI-assisted development practices. Partner with Product, QA, Design, Operations, and Engineering stakeholders to deliver reliable agentic systems.

Build AI-powered tools that accelerate nuclear engineering workflows, including safety and physics analyses, neutronics simulations, and automated ticket, design-tree, and file-management processes. The role embeds with engineering teams to identify bottlenecks, prototype solutions, and deploy production-grade Python tools using LLMs, AI agents, RAG, and tool-use architectures. Work is fully onsite, may require extended hours, and requires US citizenship with the ability to obtain and maintain a Q clearance.

Build and support enterprise AI-orchestrated workflows and API integrations across ITSM, HR, identity, access, and internal business systems. Develop Python and JSON automation logic, troubleshoot APIs and authentication, and implement logging, monitoring, retries, correlation IDs, and error handling. Review AI-generated code, support production workflows, collaborate with stakeholders, and create reusable automation templates and documentation while following security, governance, testing, and release standards.

Leads enterprise AI innovation through hypothesis-driven pilots, evaluation, and experimentation with agentic workflows, RAG, text-to-SQL, and emerging LLM techniques. Designs secure, observable, cost-aware AWS architectures, develops reference implementations, and transitions successful prototypes into scalable enterprise patterns. Communicates technical findings, business impact, tradeoffs, and recommendations while evaluating AI tools, frameworks, vendors, and research.

Partners with clients to identify AI opportunities, translate ambiguous business needs into technical requirements, and build solutions using Microsoft Power Platform and Copilot Studio. Develops prompts, grounding strategies, integrations, and prototypes involving Copilot and Claude. Supports testing, quality assurance, maintenance, troubleshooting, documentation, user training, governance, and data protection in regulated environments while building stakeholder consensus.

Designs and optimizes scalable data pipelines, machine learning models, and AI-enabled enterprise applications. Builds agentic systems, chatbots, autonomous agents, and automation tools; integrates data and AI solutions with business platforms; and ensures data quality, security, compliance, and governance. The role requires Python, SQL, cloud data platforms, modern pipeline tools, MLOps, API integration, and collaborative delivery of reliable end-to-end solutions.

Own the design, development, integration, release, and reliability of tailored AI solutions for enterprise clients. Adapt a core AI platform, deliver client-specific changes to a shared codebase, manage production rollouts, diagnose complex issues, and serve as the primary technical contact for client engineers. The role requires strong Python, software architecture, system design, and experience deploying LLM or agent-based systems used by customers in live environments.

Build and operate production AI agent systems that extract and validate data from documents and spreadsheets, integrate with external systems, manage workflow state and failures, and support human review. Develop evaluation datasets, regression tests, monitoring, and reliability improvements while balancing model use with deterministic code. Collaborate with retirement plan experts, lead technical design, write documentation, review code, and mentor engineers.

The posting provides no specific responsibilities or qualifications. The role title indicates a Full Stack AI Engineer position using .NET.

Design and optimize agentic AI systems for NVIDIA’s CUDA ecosystem, including agent architectures, multi-agent workflows, tool use, memory, orchestration, training, verification, reward, and evaluation systems. Co-design AI systems across models, inference, compilers, runtimes, libraries, kernels, and GPUs. The role requires strong C/C++, Rust, and Python skills, GPU programming expertise, and experience with agent systems, reinforcement learning, or modern AI inference.

Build and operate secure, scalable production infrastructure for agentic AI platforms across AWS and Azure. Responsibilities include CI/CD automation, infrastructure as code, cloud security, governance, monitoring, observability, model lifecycle management, drift detection, incident response, networking, resilience, and cost optimization. The role establishes production-readiness standards, supports AI deployments, and partners with engineering, product, security, compliance, and business teams.

Drive adoption of AI-powered utility solutions through pre-sales consulting and post-sales enablement. Gather requirements, architect and demonstrate computer vision and ML workflows, lead RFI/RFP support, guide deployments and integrations, complete security audits, deliver customer training, and create technical presentations. The role requires strong AI/ML knowledge, customer-facing communication, scripting experience, and willingness to travel domestically and internationally.

Develops statistical, machine learning, AI, and predictive solutions to solve complex business problems. Mines and analyzes large datasets, builds algorithms and automated data processes, validates models, and communicates insights to business and product leaders. Designs enterprise AI applications, including generative AI and RAG solutions, while supporting data governance, visualization, security, scalability, monitoring, and responsible AI practices.

Deploys and operationalizes AI solutions across the enterprise, translating business needs into technical requirements and ensuring data, infrastructure, security, governance, scalability, and adoption readiness. The role configures integrations, data pipelines, workflows, and AI services; converts prototypes into production solutions; troubleshoots deployments; enables stakeholder adoption; and creates repeatable implementation practices. It partners with business, product, data science, AI engineering, and platform teams, particularly within regulated environments.

Designs and operates production-quality AI framework, runtime, benchmarking, performance, automation, and observability components. Benchmarks, profiles, debugs, and optimizes large language model training and inference across GPUs and Microsoft hardware. The role drives projects from definition through deployment, investigates cross-stack reliability issues, partners with research, infrastructure, model, and hardware teams, contributes to technical standards, and mentors engineers.

Own the architecture and implementation of Life360’s cross-functional AI agent ecosystem. Define reusable agent standards, identity and authorization models, evaluation and regression infrastructure, execution frameworks, retrieval systems, telemetry, and cost controls. Build production components, guide build-versus-buy decisions, establish shared architecture and standards, validate systems through live use cases, and mentor senior engineers across AI Platforms, AI DevX, and Agentic PDLC.

Lead technical vision and architecture for AI frameworks, performance, benchmarking, and tooling. Drive cross-team alignment, mentor engineers, ship high-performance distributed software, and improve model onboarding, runtime performance, reliability, and Azure hardware efficiency.

Leads architecture, development, and delivery of secure cloud, data, process intelligence, and AI solutions for federal clients. Builds AWS cloud-native services, scalable data platforms, Celonis process-mining solutions, and agentic AI workflows using Amazon Bedrock and Azure OpenAI. Owns technical direction, engineering standards, security, testing, observability, deployment automation, and reusable product packaging. Supports A-123 continuous controls monitoring, agency implementations, demonstrations, and cloud environment deployments while collaborating with AWS and Celonis partners.

Develop and maintain production AI-powered software for mission-critical USPS systems. Design distributed, microservice-based architectures, improve reliability and scalability, identify bottlenecks, and modernize existing systems. Work across the technology stack, communicate architectural decisions, mentor junior developers, and use AI and automation tools to improve engineering outcomes. The role is remote and requires the ability to obtain Public Trust clearance.

Build and own Life360’s AI platform infrastructure, including agent orchestration, MCP gateways, LLM routing, evaluation systems, observability, memory, retrieval, and production inference. Establish secure, reliable, cost-effective shared AI capabilities; define standards, SDKs, and paved paths for engineering teams; collaborate with Security, Privacy, Legal, and AI-focused teams; respond to incidents; and mentor engineers on agent architecture and evaluation practices.

Build and own Supabase’s AI tooling, including agents, MCP servers, agent skills, dashboard assistants, and developer-facing AI capabilities. Establish eval-first development through benchmarks, instrumentation, quality KPIs, and regression gates. Design how documentation and product knowledge are exposed to agents, create reusable AI patterns, and collaborate across Docs, CLI, Frontend, SDK, and infrastructure teams. Improve reliability, testing, authentication, hosting, and developer experience in a fast-moving, globally distributed environment.