Top AI Engineer Jobs in Los Angeles, CA

Build and launch customer-facing AI applications by developing frontend and backend features, integrating APIs, AI services, databases, and business tools, and improving reliability and usability. Independently manage features from requirements through deployment, troubleshoot AI-generated code, and collaborate with developers. Applicants may be self-taught or early-career and should demonstrate completed applications through employment, freelance work, or personal projects.

Design, develop, test, debug, document, and deploy advanced AI/ML applications. Collaborate with AI/ML and software engineers to deliver solutions in development and production environments, develop software and testing requirements, and support rapid prototyping and demonstrations. The role involves LLMs, NLP, computer vision, sensor fusion, explainable AI, and responsible AI for advanced aerospace and national security systems.

Build AI-powered automations, internal tools, bots, microservices, and web applications that streamline workflows across business functions. Integrate enterprise systems, APIs, LLMs, and agent frameworks; translate stakeholder pain points into technical solutions; monitor production automations; and improve reliability and performance. The role targets early-career software engineers who can communicate with nontechnical users and rapidly ship secure, impactful tools.

Own the architecture and technical strategy for Square’s go-to-market systems. Build and operate production AI agents, automated workflows, and integrations that improve sales productivity. Lead complex initiatives through architecture, implementation, testing, deployment, and operations while establishing AI evaluation, observability, security, reliability, and human-oversight practices. Partner with Sales, Product, Data, and Engineering leaders to translate business needs into scalable solutions, make build-versus-buy decisions, mentor engineers, and improve business impact through data and user feedback.

Build and own Lightspark’s internal AI and data platform, including agent infrastructure, MCP servers, tool integrations, governed data, sandboxed execution, and production pipelines. Partner with Finance, Sales Ops, Compliance, and other teams to replace manual workflows with reliable systems. Extend dbt and cloud data warehouse infrastructure, support monitoring and data reliability, and improve engineering tooling. The role requires production experience with AI agents, LLM APIs, internal platforms, and TypeScript/Node.js or Python.

Build and own conversational voice AI products end-to-end: architect, implement, and deploy the platform. Design infrastructure, develop front-end and back-end systems, deliver reliable customer outcomes, and collaborate directly with the CTO to scale the product for enterprise customers.

Design, build, evaluate, and deploy production AI and ML solutions, including generative AI, large language models, retrieval-augmented generation, and agentic workflows. Develop reliable software, APIs, integrations, evaluations, and observability capabilities while partnering with architecture, platform, data, cybersecurity, and business teams. Apply AI engineering practices across enterprise, cloud, hybrid, and regulated environments.

Designs, builds, and operates reusable enterprise AI platform services, backend APIs, model-serving capabilities, agent runtimes, retrieval services, and MLOps infrastructure. The role automates cloud-native deployments across hybrid and restricted environments using Kubernetes, CI/CD, and infrastructure-as-code. Responsibilities include implementing security, identity, observability, lifecycle management, scalability, resilience, and cost visibility for AI workloads while partnering with architecture, application engineering, cybersecurity, data, and product teams.

Founding Engineer responsible for building FilmoreAI’s production AI and data platform for construction equipment dealers. The role covers agent infrastructure, model routing, LLM extraction and reasoning, canonical data modeling, entity resolution, Python data pipelines, Azure cloud orchestration, evaluation, observability, and safe human-approved write-back. The engineer will work independently in an ambiguous startup environment and use agentic development tools to ship reliable systems with strong cost, latency, accuracy, and operational guardrails.

Build, maintain, and improve production AI services for document understanding including LLM pipelines, background jobs, databases, and internal tools. Implement features, debug production issues, write tests, and contribute to evaluation frameworks to improve model quality and system reliability. Collaborate with a small engineering team and take increasing ownership over platform components.

Fine-tune and align LLMs for specialized financial tasks; build agentic solutions with LangChain/Autogen; engineer ML pipelines for large financial datasets; deploy, monitor and scale production models; track AI research and integrate emerging tooling.

Design, build, and maintain production-grade AI-powered go-to-market automations (prospecting, lead scoring, campaign orchestration, attribution). Integrate ML/LLM tools into marketing and sales workflows, track business-critical metrics, ensure reliability and scalability, and collaborate with GTM strategists and non-technical stakeholders to drive measurable pipeline and revenue outcomes.

Build and deploy AI-powered applications and agents for engineers and operators across the rocket manufacturing organization. Responsibilities include full-lifecycle software development, foundation-model integration, retrieval and vector search, evaluation, reliability, testing, continuous integration and deployment, and collaboration with users to define and iterate on solutions. The role uses modern AI, data, cloud, and platform technologies in manufacturing and physical-world environments.

Build and deploy AI-powered applications and agents used by engineers and operators. Responsibilities include full-cycle software development, foundation model integration, retrieval and tool use, evaluation, reliability, continuous delivery, and collaboration with non-software stakeholders. The role involves shipping production-quality systems across cloud and on-premise infrastructure while iterating on solutions for rocket manufacturing and launch operations.

Develop and maintain MCP tools and the MCP Toolbox for Databases; expand test coverage for agentic coding assistants; refactor and maintain test frameworks; convert technical posts into production-ready sample code and documentation; build real-world data applications (QueryData) to support product R&D and collaborate with cross-functional teams to accelerate agentic AI adoption.

Create end-to-end computer-use tasks for AI agents in Linux desktop environments. Design realistic multi-application scenarios, prepare files and instructions, write Python setup and automated scoring scripts, test tasks, and document them. The contract requires daily check-ins, rapid feedback, and availability throughout a three- to four-week engagement. Experience with AI tools, AI/ML projects, Linux VMs, Git, reinforcement learning environments, or computer-use benchmarks is relevant.

Build, integrate, enhance, and support production-grade Generative AI and LLM applications. Develop AI agents, RAG capabilities, APIs, and enterprise integrations using JavaScript and TypeScript. Apply prompt engineering, model evaluation, monitoring, security, authentication, and responsible AI practices. Troubleshoot complex issues across application, integration, model, and platform layers while supporting CI/CD, testing, deployments, observability, and production operations.

Work across AI, backend, and frontend: curate and clean writing datasets, fine-tune large language models, run training experiments, develop metrics and eval sets, build backend services in Python/Django, and implement frontend features with React.

Design and operationalize enterprise AI governance capabilities across the AI lifecycle. Translate policies, privacy, security, risk, and compliance requirements into technical controls, guardrails, automated workflows, monitoring, observability, and audit evidence. Develop AI use-case registration, classification, taxonomy, risk assessment, and shadow-AI management capabilities. Partner with Privacy, Legal, Cybersecurity, Supply Chain, and Enterprise Architecture teams to implement responsible, secure, and compliant AI 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.

Integrate and fine-tune existing AI models within production software, evaluate model performance, build evaluation frameworks, configure observability, develop Python AI features, and troubleshoot real-world performance. Collaborate with data science, engineering, the CTO, and cross-functional teams to improve products and identify AI innovation opportunities. This role focuses on applying existing models rather than designing new machine learning models.

Senior AI/ML Engineer responsible for translating federal supply chain and logistics problems into analytical solutions, developing forecasting, classification, and NLP models, and deploying them into production. The role owns model development, data pipelines, monitoring, retraining, drift detection, validation, documentation, and stakeholder communication. Work includes operating within government-controlled environments and meeting federal bias evaluation and documentation requirements.

Own the technical design and delivery of multi-component AI systems, including LLM-backed features, agent tool orchestration, evaluation frameworks, data pipelines, and model harnesses. Build reliable APIs, SDKs, and feedback loops; optimize latency and cost; assess external models and datasets; and support production systems through on-call rotation. Mentor engineers and collaborate across search, location intelligence, and platform teams.

Direct autonomous coding agents on production work, review agent-generated pull requests, author evaluation suites, and own software quality across correctness, performance, security, and WCAG accessibility. The engineer will operate within a FedRAMP-authorized environment, maintain secure agent boundaries, advance autonomy based on reliability data, improve development lifecycle patterns, and mentor engineers adopting agent-directed delivery.

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.