AI Engineer jobs in Los Angeles, CA

Define and implement agentic AI architectures and policy-as-code standards to optimize cloud financial management across multi-cloud environments. Lead delivery squads in executing FinOps automations, forecasting, and executive reporting to drive measurable business value.

Design and develop intuitive graphical user interfaces for AI agents, including conversational and multimodal interactions. Build interactive dashboards and client-facing applications that integrate with backend services, LLMs, and enterprise systems.

Build and own the frontend surfaces for AI agents that analyze hardware telemetry data. Design interaction patterns for agentic UX, including streaming responses and long-running tasks, while ensuring high performance with large datasets.

Build and own the frontend surfaces for AI agents that analyze hardware telemetry, focusing on streaming interactions and high-performance data rendering. Collaborate with customers and backend engineers to turn complex review workflows into dependable agent capabilities.

You will lead the technical direction and operational strategy for Nscale's front-end AI infrastructure networks, ensuring high performance and scalability. This includes designing large-scale Ethernet fabrics, acting as a senior escalation point for complex incidents, and mentoring engineering teams.

You will build and demonstrate connected AI solutions by integrating products, models, and agents to deliver measurable customer outcomes. This role involves creating working prototypes, API integrations, and compelling business stories to communicate technical value to executives and customers.

The AI Engineer will build, validate, and operationalize AI and analytics solutions to support revenue cycle and billing needs. They will also lead requirements analysis, create custom reports, and provide data-driven insights to improve organizational decision-making.

Own the operational health, configuration consistency, and performance of large-scale Ethernet front-end networks supporting AI inference, management, and storage workloads. Lead complex incident response and postmortems, improve automation and monitoring with SRE and engineering teams, and tune network performance and reliability.

The AI Platform Engineering Lead drives the strategy, architecture, and governance for AI/ML infrastructure, including GPU clusters, RAG pipelines, and agentic AI systems. They manage the AI Platform Operations team, oversee budgets, and ensure scalable, secure, and compliant platform operations across the enterprise.

Develop and test full stack, cloud-native applications on AWS, translating client needs into solutions across architecture, design, implementation, and testing. Troubleshoot application and platform issues, apply AI-enabled engineering methods, collaborate with engineering teams, and review work while mentoring junior colleagues.

Provide senior technical leadership for Analytics Engineering, shaping data foundations, modeling standards, architecture, governance, quality, tooling, and developer experience. Build trusted canonical datasets, semantic layers, and AI-enabled analytical tools that support self-service analytics and improve decision-making across the company.

You will design, implement, and deploy scalable AI solutions while partnering with clients across various industries to address complex business challenges. Additionally, you will provide thought leadership in scientific communities and guide non-technical teams in software development best practices.

The Data Engineer will build and maintain low-level data pipelines using Databricks and Apache Spark while coordinating with onshore and offshore teams. They are also responsible for supporting deployment, post-production maintenance, and communicating project status to engagement managers.

You will design, develop, and maintain scalable backend services and autonomous AI agents to support financial and operational workflows. Additionally, you will lead system design decisions, mentor junior engineers, and ensure the performance and reliability of production-grade AI platforms.

The SDE 3 will design and deploy multi-agent AI workflows while owning the end-to-end lifecycle of complex services and integrations. They will also provide technical leadership by mentoring junior engineers and driving internal AI adoption strategies.

The role involves designing and securing AI-enabled products and cloud environments using Zero Trust principles and secure software development lifecycles. You will collaborate with stakeholders to implement production-quality AI security architectures and automated threat management capabilities.

The engineer will design and implement secure architectures for generative and agentic AI, applying Zero Trust controls across cloud environments and development pipelines. They will also lead client discovery sessions and develop production-quality AI security platforms while mentoring team members.