Top AI Engineer Jobs in Los Angeles, CA

The role involves developing AI-powered features, including agent capabilities, ensuring their reliability and scalability, and collaborating with cross-functional teams. Candidates should have strong hands-on experience with LLMs and Python in production environments.

The Full Stack AI Engineer will develop and maintain AI agents, build clinician-facing products using React and Python frameworks, and manage data infrastructure to support clinical workflows.

Design and build autonomous, enterprise-grounded AI agents and workflows using the Elastic Stack, RAG, and LLMs. Integrate models with internal APIs and SaaS, manage deployment and scaling on cloud/Kubernetes, ensure security/compliance, document systems, and provide technical leadership for agent lifecycle and performance optimization.

The Senior AI Engineer designs and deploys AI agents and workflows, collaborating across teams to deliver reliable AI solutions that drive business outcomes.

The Senior AI Developer will build and maintain production-grade AI solutions, manage data pipelines, and collaborate on technical standards within the AI Operating Group.

The AI Engineer will design backend systems for workflows, optimize multi-agent pipelines, develop knowledge retrieval systems, and collaborate to integrate workflows using LLMs and traditional systems.

Maintain and expand the companys foundational AI platform codebase; support project teams to increase reuse (50-70%); fold successful project work back into the platform; address bugs and performance issues; create training, documentation, and onboarding; provide pairing and code reviews; and collaborate with sales and delivery teams on platform capabilities and project requirements.

Lead client-facing AI engineering engagements: build and deploy production LLM systems using RAG, vector DBs, and agent frameworks; handle cloud deployments, CI/CD, and reliability; debug enterprise issues (auth, networking, data); embed with client teams and document solutions for reuse.

Lead and deliver an enterprise AI platform and agent factory: design architecture, build cost-effective model infrastructure, harden security/SSO/IAM, enable developer adoption, and build agent workflows across the PDLC (ideation, development, deployment). Mentor engineers, steer delivery partners, and remain hands-on implementing agents, RAG pipelines, integrations, and observability to drive measurable productivity gains.

Build and deploy production AI capabilities for mental healthcare: full‑stack development, agentic systems, RAG and retrieval pipelines, evaluation frameworks, safety guardrails, observability, CI/CD, and cross‑functional collaboration to ensure safe, reliable AI in clinical workflows.

Lead design and build scalable autonomous and semi‑autonomous multi‑agent AI systems. Implement RAG pipelines, tool‑calling agents, memory, observability, and AgentOps. Ensure production readiness, safety, governance, and secure data handling. Provide technical leadership, mentor engineers, and shape platform standards across teams.

Design, develop, and optimize prompts for conversational AI and integrate them into NLP/LLM systems. Build RESTful APIs (Python/Django/Flask) and React front-ends, evaluate prompt effectiveness with metrics and user feedback, collaborate with product, UX, and data teams, and document prompt engineering methodologies.

Design, build, and deploy production-grade Generative AI applications: multi-agent systems, advanced RAG pipelines, LLM integration and swapping, model fine-tuning and quantization, scalable FastAPI backends, containerized deployment, and monitoring/optimization for latency, cost, and reliability.

Build and deploy generative AI solutions: develop production-grade Python code, LLM-based systems, agent frameworks, RAG pipelines with vector databases, APIs, cloud deployments, and MLOps practices while ensuring model evaluation, guardrails, and observability.

Design, build, and deploy agentic AI and multi-agent systems using LangChain/LangGraph and ADKs. Implement LLM orchestration, RAG pipelines, vector DBs, React-based AI interfaces, and production-grade infrastructure with observability, security, and optimization.

Lead architecture and implementation of autonomous inbound/outbound AI agents, build scalable LLM ops and signal-intelligence data pipelines, integrate identity resolution and third-party CRM systems, and maintain production-ready, well-documented systems for Series B diligence.

Design, develop, and maintain generative AI and cloud-based AI solutions using Microsoft Azure and Power Platform tools. Collaborate with cross-functional teams to gather requirements, build scalable AI applications, deploy and manage models with Azure Machine Learning, test/debug, document designs, and provide training and user support.

The Senior AI Engineer will design and maintain customer-facing AI tools by building APIs, optimizing workflows, and conducting experiments with generative AI models, ensuring system reliability and integration with production systems.

Design, build, and ship AI-powered observability features using LLMs and agentic workflows. Rapidly prototype, iterate with real users, integrate AI into alerting, runbooks, and developer workflows, and ensure solutions are scalable, maintainable, and production-ready while collaborating cross-functionally.

Lead design, deployment, and monitoring of LLM-based AI solutions. Build end-to-end generative AI applications, fine-tune models, integrate LLM APIs into products, ensure post-deployment performance, and collaborate cross-functionally within an Agile environment to drive measurable business impact in healthcare.

Lead design and implementation of production agentic AI systems that integrate LLMs with Teak's platform APIs. Build RAG pipelines, MCP/tool integrations, evaluation frameworks, and AI infrastructure on cloud (AWS/GCP/Azure). Contribute backend services in Python, ensure compliance and safety, and operate systems in production including observability and on-call duties.

As a Staff Backend AI Engineer, you will design and operate backend AI systems for Arbiter's care orchestration platform, ensuring reliability, low latency, and scalability while collaborating across teams and mentoring junior engineers.

The Senior AI Engineer will develop LLM-powered AI systems, build RAG pipelines, and solve complex information retrieval problems while collaborating cross-functionally to enhance Affinity's CRM platform.

The Staff AI Engineer will drive architecture and implementation of AI-assisted engineering practices, overseeing technical standards, automated testing, and code review systems for software development in a regulated fintech environment.

The AI Engineer will design, develop, and deploy AI-powered systems, improve operations with intelligent workflows, and collaborate on high-leverage AI opportunities.