Top AI Engineer Jobs in Los Angeles, CA

Design and deliver AI-enabled features for web workflow applications using LLMs and agentic patterns. Build POCs and production-ready RAG pipelines, implement orchestration frameworks, integrate LLM APIs, establish observability and guardrails, maintain full-stack apps, participate in CI/CD and code reviews, and document technical implementations.

Act as a part-time external technical mentor for Claude/agentic-AI programs: run 1:1 calls, technical deep-dive presentations, and group Q&A; review student projects, provide guidance on Claude ecosystem, agent design, prompts, production AI systems, and best-practice engineering.

The Principal AI/ML Engineer will develop machine learning models for campaign optimization in AdTech, collaborating across engineering and product teams to enhance advertising capabilities.

Lead the product strategy for Scribd's internal developer and agentic platforms: unify AI workstreams, own internal app/plugin/agent runtimes, translate functional needs into platform requirements, drive cross-functional adoption and metrics, and run internal rollouts and evaluation to prove impact.

Design, build, and evaluate AI systems, focusing on natural language understanding and automation. Collaborate with teams to ensure production quality and performance metrics.

Lead design, build, deployment, and maintenance of production-grade GenAI systems (LLM apps, agentic workflows, RAG, retrieval, and evaluation). Architect scalable ML services, implement model serving and observability, optimize latency and cost, fine-tune LLMs, translate prototypes to production, mentor engineers, and produce runbooks and documentation.

Support development and evaluation of AI features and RAG pipelines: prompt experimentation, data preparation, embedding generation, model evaluation, debugging, and basic AI service development using Python and ML tools under senior engineer guidance.

Design, build, and optimize GPU-centric multi-agent AI systems and runtimes for the CUDA ecosystem. Co-design solutions with hardware, compiler, and algorithm teams, develop evaluation frameworks for performance and quality, and collaborate across the AI stack to accelerate planning, tool-use, model inference, and developer productivity.

Design, implement, and optimize explainable clinical reasoning AI systems that integrate patient data and medical knowledge. Build RAG/embedding pipelines, backend integrations, scalable Docker/cloud-native deployments, and ensure transparent, evidence-backed model outputs while collaborating with cross-functional teams.

Design, build, and deploy AI-powered workflows, custom agents, and RAG pipelines; integrate LLM APIs into internal platforms; prototype emerging AI tools; collaborate cross-functionally; maintain code, documentation, and an AI roadmap; provide training and act as internal applied AI subject-matter expert.

Design, build, and ship agentic LLM systems and orchestration pipelines. Translate stakeholder ideas into production workflows, write and refine prompts, run evals and observability in Weave or similar, and maintain system performance and guardrails.

Build and productionize GenAI features: design experiments, evaluation suites, advanced RAG and reasoning pipelines, oversee SFT/LoRA tuning, and optimize quality-cost-latency trade-offs for reliable, user-focused AI products.

Build and ship AI-driven GTM systems: outbound lead engine, inbound/lifecycle automations, AI-assisted content pipeline, data/reporting layer, and fast production patches to eliminate manual marketing workflows.

Design, build, deploy, and maintain scalable, resilient backend services for a Voice AI platform. Lead cross-cutting projects, make architectural decisions, integrate AI platforms, implement observability and evaluation frameworks, and mentor engineers.

Design, build, deploy, and iterate production-ready AI agents and LLM-based solutions that integrate with enterprise workflows. Rapidly prototype, evaluate, and optimize agents for performance, cost, and adoption. Partner across functions to redesign workflows, contribute to an AI Center of Excellence, and ensure responsible, secure, and scalable agent development.

Design, build, and scale production-grade AI applications using LLMs and RAG architectures. Develop backend services and integrations, deploy and optimize solutions on AWS/Azure/GCP, and implement containerization with Docker and Kubernetes. Collaborate across teams, contribute to architecture, code reviews, documentation, and ensure production reliability and scalability.

Design and deliver agentic systems for enterprises, integrating tools and data while ensuring safety and compliance. Collaborate with clients to optimize business workflows and system architectures.

As a Forward Deployed AI Engineer, you will develop and implement voice AI solutions for enterprise clients, ensuring system reliability and customer satisfaction while managing technical discussions and deployments.

Lead architecture, design, development, deployment, and optimization of an agentic AI platform. Partner with product, data, security, and business teams to productionize AI solutions, establish engineering best practices, mentor teams, and drive reusable AI services, automation, and operational improvements to deliver business impact.

The Software Architect will lead AI quality evaluation, design evaluation frameworks, create risk controls, and ensure compliance, partnering with various teams on AI systems.

Design, build, and operate large-scale LLM infrastructure and data platforms for training, fine-tuning, and inference. Provision GPU clusters, optimize GPU utilization, implement model lifecycle management, deploy inference frameworks, create evaluation and observability systems, and collaborate with data scientists to productionize AI capabilities. Mentor engineers and enforce MLOps/DataOps best practices.

Design, build, and deploy AI agents and multi-step automation workflows for Client Experience. Integrate systems via APIs, partner with stakeholders, ensure governance and security, and track metrics for efficiency and customer outcome improvements.

Develop AI solutions for marketing teams, integrating AI agents and workflows while coaching teams for self-sufficiency in AI use.

As a Principal Data Engineer, you will design and implement LLM, AI-powered security data platforms, mentor engineers, and drive the adoption of data solutions across teams.

Unpaid AI engineering internship to design, build, and deploy ML solutions (NLP, computer vision, recommenders). Work on data preprocessing, model training/fine-tuning, containerized FastAPI microservices, and scalable deployments on Google Cloud Run. Contribute to code reviews, GitHub repos, and integration of third-party AI APIs.