Top AI & Machine Learning 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.

Build and optimize cloud-native applications on Google Cloud across the software development lifecycle. Develop generative AI solutions using Vertex AI, LLM APIs, Python, prompt engineering, RAG, LangChain, and LangGraph. Test application and model performance, support LLM evaluation and guardrails, translate requirements into technical components, and document implementation decisions for client teams.

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 client-facing AI adoption and productivity initiatives, designing Azure-based generative AI solutions, prompts, agent workflows, and workforce analytics models. Oversees LLM evaluation, guardrails, governance, deployment, and adoption measurement. Manages engagement workstreams, budgets, timelines, deliverables, client relationships, and team coaching while guiding training and AI-human collaboration programs.

Supports client cloud modernization engagements by designing and building Google Cloud data and generative AI solutions. Develops Python workflows for prompt engineering, retrieval-augmented generation, agent orchestration, and LLM evaluation. Configures Vertex AI, LLM APIs, vector databases, guardrails, and LLMOps processes while applying machine learning and NLP concepts. Collaborates on data platform and analytics modernization, troubleshooting, research, solution design, documentation, and iterative improvement with senior staff.

Build, test, and optimize Google Cloud-native applications and generative AI solutions across the software development lifecycle. Translate client needs into technical designs, develop AI-enabled features using Vertex AI, LLM APIs, prompt engineering, RAG, Python, LangChain, and vector databases, and evaluate model performance and guardrails. Collaborate with stakeholders, review engineering deliverables, manage workstreams, and mentor junior team members.

Leads architecture and delivery of AI-powered pharmaceutical products, establishing AI-first engineering, testing, evaluation, LLMOps, security, and auditability practices. Partners with product managers and client technology stakeholders, oversees offshore data science, engineering, QA, and DevOps teams, and makes scalable platform decisions. Also directs Adobe-focused analytics initiatives involving data pipelines, statistical modeling, predictive analytics, visualization, KPI frameworks, and executive recommendations.

Leads multidisciplinary teams delivering AI-enabled, cloud-native applications on AWS. Oversees architecture, development, testing, deployment, modernization, platform engineering, security, reliability, and client advisory activities. Translates business objectives into technical roadmaps, guides complex engineering work, coordinates cross-functional architecture reviews, communicates delivery risks and options, and coaches teams across multiple workstreams.

Designs, builds, tests, and troubleshoots full-stack cloud-native applications on AWS. Translates client needs into technical solutions across architecture, development, testing, and delivery; builds scalable infrastructure and integration patterns; applies AI-enabled engineering methods; and supports modernization initiatives. The role also reviews technical work, mentors junior engineers, collaborates with architecture teams, and helps deliver cloud, data, and analytics solutions.