Top AI Engineer Jobs in Los Angeles, CA

Designs, develops, trains, tests, and deploys secure AI and machine learning solutions for enterprise and government environments. Responsibilities include preparing datasets, identifying automation opportunities, evaluating model performance and security, documenting risks and controls, and collaborating with data scientists, software developers, cybersecurity engineers, architects, and domain experts. The role applies AI and ML to enterprise security engineering, testing, and mission workflows.

Develops, trains, tests, deploys, and maintains production computer vision and machine learning models for mission-critical applications. Responsibilities include deep learning, transformer-based vision models, sensor fusion, target tracking, GPU optimization, embedded systems, and real-time missile defense software. The engineer collaborates with multidisciplinary space, military, intelligence, engineering, and AI teams to design and implement robust operational capabilities.

Develop and deploy AI-native systems supporting legal and compliance operations. Build production RAG and agentic AI solutions, including retrieval, memory, tool use, multi-agent workflows, and evaluation frameworks. Partner with Legal, Compliance, and the MLRO to translate regulatory and operational needs into scalable tools. Conduct independent research, prototype solutions, benchmark system performance, and support compliance reviews, remediation tracking, product launches, and related operational activities.

Design, build, and deploy autonomous AI agents using LLMs and agent frameworks; integrate agents into e-commerce and supply chain systems; implement multi-agent orchestration, memory and tool integrations; fine-tune models and apply RL concepts; and build safety, monitoring, and MLOps pipelines.

Build production AI agents and agent-powered features from research and prototyping through deployment. Develop evaluation infrastructure with golden datasets, regression suites, and offline/online scoring. Own retrieval and context pipelines, production instrumentation, failure taxonomies, and cost and latency budgets. Partner with data science and product management on experiments and agent behavior, while establishing LLM engineering patterns, guardrails, and reusable tooling.

Build and deploy generative AI applications and multi-agent systems on Google Cloud using Vertex AI, Python, BigQuery, RAG, and secure enterprise data integrations. Develop data pipelines, train and serve models, evaluate AI output, protect sensitive information, troubleshoot production systems, and collaborate with technical and business stakeholders.

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 implement AI/ML models; analyze large datasets; integrate AI solutions into existing systems; apply MavLink for autonomous systems; collaborate across teams and improve ML development processes.

Build and deploy agentic AI systems that automate customer experience operations, including reporting, QA analysis, case workflows, SLA risk detection, and knowledge maintenance. Develop orchestration layers, integrations, memory architecture, retrieval pipelines, and escalation logic. Measure AI reliability and business outcomes while partnering with data, QA, workforce management, and customer support teams. Productize successful tools for organization-wide scale.

Design, build, and launch AI-focused software capabilities using Python and distributed-systems principles. Own initiatives from solutioning and architecture through production implementation and launch while remaining hands-on with code. Collaborate with Nextiva engineering and product teams, Google Gemini, and other external technology partners to extend AI capabilities across partner environments and the Nextiva ecosystem. Solve ambiguous early-stage technical problems and contribute to system design and architecture decisions.

Build and ship production-grade AI features by embedding with clinical, product, and operations teams. Own solutions end to end, including architecture, evaluation, validation, governance, deployment, monitoring, and human-in-the-loop safeguards. Develop agentic capabilities using tool calling, RAG, MCP integrations, and orchestration on AWS Bedrock. Establish acceptance criteria and risk controls, contribute reusable platform assets, and improve AI fluency across teams. The role requires strong Python, cloud engineering, full-stack development, and experience delivering validated AI features in production.

AI/ML Engineers will build and deploy AI-native products, autonomous agents, LLM applications, RAG systems, and production ML pipelines. Responsibilities may include integrating AI into crypto infrastructure and consumer products, fine-tuning and evaluating models, optimizing inference, and developing agentic workflows and machine-to-machine payment systems. Engineers will work end-to-end with high-ownership teams in fast-moving environments.

Build and operate production-quality AI framework, runtime, benchmarking, performance, automation, observability, and developer-tooling systems. Optimize large language model training and inference across GPUs and Microsoft hardware, diagnose cross-stack reliability and performance issues, and improve model onboarding, hardware utilization, Azure efficiency, and deployment speed. Senior engineers own major components and projects; Principal engineers define technical direction, architecture, and multi-release strategy while leading cross-team initiatives and mentoring technical leaders.

Leads enterprise AI-native engineering initiatives by researching and prototyping AI tools, designing AI-enhanced workflows, creating reference architectures, and developing proofs of concept. Owns AI training and enablement, including documentation, presentations, tutorials, demonstrations, and coaching. Evaluates tooling, supports adoption, communicates technical changes to leadership, hosts engineering office hours, and establishes scalable standards across the organization.

Design and build production AI applications for enterprise clients, including data processing, retrieval, APIs, tools, integrations, evaluations, and deployment. Partner with client experts to improve system performance and reliability, support cloud, CI/CD, security, and operational processes, and communicate technical tradeoffs clearly. The role also involves automating internal workflows and helping clients adopt new AI-enabled ways of working.

Build and deploy production AI/ML and GenAI systems across the full model lifecycle. Develop RAG pipelines, LLM integrations, data pipelines, labeling workflows, evaluation frameworks, and MLOps infrastructure. Apply AI to manufacturing, computer vision, predictive maintenance, supply chain, forecasting, inventory, supplier risk, and logistics. Collaborate with engineering, manufacturing, and operations teams to deliver measurable business outcomes.

Design, build, deploy, monitor, and optimize production machine learning and LLM systems, including RAG pipelines, agentic workflows, fine-tuned models, and embeddings. The role owns technical delivery from architecture through iteration, collaborates with enterprise clients, mentors junior ML engineers, conducts code reviews, and helps establish internal engineering standards and tooling.

As an AI Demo Engineer, you will create demo applications to showcase AI capabilities, collaborate with sales, and enhance demo content.

Develop and integrate production AI/ML capabilities into Oracle E-Business Suite on OCI for federal financial management. Build NLP and document-processing solutions, REST API integrations, ML lifecycle workflows, cognitive performance metrics, and data-mining capabilities. Participate in SAFe Agile delivery, author user stories, support DevSecOps integration, resolve technical impediments, and contribute to transition and knowledge-transfer materials. The role requires collaboration with architecture, product, and government teams in a cleared defense environment.

Build and deploy production-grade agentic AI workflows for Fan Ops and Marketplace Ops, including issue triage, response drafting, dispute resolution, risk scoring, and SLA monitoring. Integrate internal and external data systems, create usable interfaces and retrieval tools, coach nontechnical operators, and own deployment, monitoring, iteration, and impact measurement. The role also evaluates emerging LLM tools and establishes AI-embedded engineering practices across Operations.

Owns the strategy, architecture, and implementation of production AI and machine learning systems for personalization, search ranking, relevance, and patient-provider matching. Builds recommendation engines, retrieval models, LLM applications, and scalable ML infrastructure while establishing AI safety, evaluation, and MLOps standards. The role requires hands-on model development, technical leadership, mentoring, and responsible deployment of AI in a regulated mental healthcare environment.

Design, build, and productionize LLM-powered systems (adaptive content, tutors, evaluators) and AI pipelines (RAG, embeddings, fine-tuning). Prototype and optimize models across modalities, define AI safety/evaluation/observability practices, and collaborate with product and engineering to measure learner outcomes and iterate.

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.

Build and support AI agents, automated workflows, system integrations, and generative AI solutions using Azure AI, Copilot Studio, Power Automate, Tines, and supporting code. Collaborate with stakeholders to define requirements, test and deploy reliable automations, implement logging and security, troubleshoot production issues, document solutions, and measure business outcomes. Integrate platforms such as Teams and Salesforce through APIs, webhooks, and connectors while validating AI-generated outputs.

Build and deploy production AI/ML features across the model lifecycle, including GenAI systems, RAG pipelines, data pipelines, evaluation infrastructure, and MLOps. Apply machine learning to manufacturing, supply chain, computer vision, predictive maintenance, forecasting, inventory, and logistics problems. Collaborate with engineering, manufacturing, and operations teams to deliver measurable outcomes.