Top AI Engineer Jobs in Los Angeles, CA

Evaluate and improve coding agents by assigning real software engineering tasks, reviewing model-generated work, completing real coding problems, and identifying model failures. The role is a fixed-pay contract offering $6,000 per week for 40 hours, with flexible scheduling and ongoing weekly work. Candidates need at least four years of professional software engineering experience, a computer science or related degree, U.S. work authorization, strong written English, and availability for 40 hours weekly.

Lead the architecture, development, evaluation, and operation of enterprise autonomous AI agents integrated with Salesforce. Build stateful multi-agent workflows using LangGraph, Claude, and MCP; implement evaluation, observability, security, and PII protection; optimize model routing and performance; and establish engineering standards, API contracts, and architecture roadmaps. The role requires extensive software engineering and production LLM experience, strong debugging and stakeholder communication skills, and advanced English proficiency.

Develop and enhance enterprise AI agents, orchestration workflows, conversational applications, and AI-powered automation using Python, LangGraph, and LangChain. Integrate AI solutions with enterprise systems and APIs, implement planning, memory, tool-calling, observability, and monitoring capabilities, and support production-grade generative AI platforms. Collaborate with engineering teams and stakeholders on architecture, reliability, performance, testing, and scalable AI engineering practices.

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, and end-to-end ML pipelines. Define AI/ML use cases, transition roadmaps, cognitive adoption metrics, and data-mining strategies. Participate in SAFe Agile delivery, write user stories, remove technical impediments, support DevSecOps integration, and produce transition and knowledge-transfer materials.

Design, implement, and operate production-quality AI framework components, benchmark and optimize large model training and inference across GPUs and Microsoft hardware, build automation and observability, drive cross-team projects from definition to deployment, and provide technical leadership and mentorship for improvements in performance, reliability, and model onboarding velocity.

Build and operate the inference and orchestration backend powering AI interactions across mobile and desktop applications. Responsibilities include designing production AI services, optimizing latency and throughput, managing caching, batching and streaming, and maintaining monitoring, logging, alerting, and incident response systems. The role requires reliable APIs, distributed-systems debugging, and scalable integration with LLMs, embeddings, and multimodal models.

Own the architecture and technical direction of Distru’s AI product suite. Build and operate production multi-agent systems, MCP integrations, contextual memory and unified data layers, evaluation frameworks, observability, reliability, and safe workflow execution. Establish engineering standards, prototype emerging agentic approaches, collaborate with customers and product teams, and influence roadmap and technical direction.

Develops reusable AI platform components, agent frameworks, connectors, evaluation systems, and developer workflow tools. Builds and tests LLM-enabled applications, implements LLMOps practices, monitors production services, and applies security and governance controls. Partners with software engineering teams to improve AI-assisted delivery through templates, integrations, documentation, onboarding, and technical support.

Build, configure, deploy, monitor, and optimize production AI agents using Microsoft AI Foundry. Develop shared agent tools, customize prompts and tools, implement hosted agents when needed, and enforce security, identity, evaluation, cost, and responsible AI standards. Partner with AI specialists and business experts to test and improve agents, coach non-engineers on prompt evaluation, integrate enterprise systems such as Salesforce and Databricks, and contribute to scalable Agent Factory practices.

Develop and maintain Python-based AI applications under senior guidance. Collaborate on requirements and feature implementation, use Pydantic for data validation, write documented code, participate in reviews, troubleshoot issues, and support testing and CI/CD. The role requires Python, object-oriented programming, REST APIs, and knowledge of LLMs, with opportunities to learn modern frameworks, backend development, data engineering, and cloud platforms.

Build and operate production services for AI agents, including internal tools, customer integrations, and reusable primitives for meetings, browsing, search, email, and payments. Design reliable agent interfaces and workflows, collaborate with product and engineering teams, and improve platform safety, observability, testing, security, and operational reliability.

Use AI tools as a primary software development partner to generate, test, document, debug, and refactor production-grade applications. Translate requirements into prompts, implementation plans, and acceptance criteria; validate AI-generated code for correctness, security, quality, and maintainability; create automated tests and reusable AI workflows; collaborate with product, QA, DevOps, architecture, and security teams; and guide engineers in adopting AI-native development practices.

Build, deploy, monitor, and improve production-grade AI agents using Python, Claude Agent SDK, and AWS Bedrock AgentCore. Design golden datasets and LLM-as-a-Judge evaluations, integrate automated quality gates into CI/CD, and implement Langfuse or OpenTelemetry observability. Evaluate agent accuracy, task completion, tool execution, and performance while supporting RAG pipelines and AWS data workflows. The role also involves troubleshooting, continuous evaluation, and potentially agentic red-teaming.

Lead AI Engineer defines AI strategy, architecture, and standards; makes build vs. buy decisions; architects scalable, reliable LLM/GenAI systems; establishes LLMOps, evaluation, guardrails, and observability; coordinates cross-functional alignment with product and engineering; and champions AI-assisted developer tooling and responsible AI practices.

Leads the design, development, testing, and evaluation of AI and machine learning solutions. Directs data analysis, modeling, infrastructure, monitoring, and integration efforts while establishing standards for AI robustness, bias, explainability, failure modes, and technical controls. Manages large projects, advises clients, coaches teams, and develops reusable evaluation tools and evidence workflows for responsible AI deployment.

Provides technical leadership for AI framework and model-serving initiatives at cloud scale. Owns architecture, roadmaps, performance optimization, benchmarking, observability, and multi-release execution across models, compilers, runtimes, services, GPUs, and silicon. Leads cross-team alignment, makes architectural decisions, mentors engineers, performs hands-on implementation and debugging, and drives measurable improvements in model onboarding, runtime performance, reliability, hardware utilization, and Azure capacity efficiency.

Designs and supports enterprise data platforms, pipelines, models, Power BI reporting, and AI-enabled applications. Develops ETL/ELT integrations, semantic models, dashboards, data governance controls, and scalable analytics solutions. Partners with stakeholders to identify AI use cases, build proofs of concept, and deploy solutions using Python, Azure OpenAI, LLMs, RAG, and AI agents. The role also optimizes performance, establishes responsible AI practices, and supports cloud and hybrid data environments.

Build scalable AI safety infrastructure, agentic automation loops, safety models, content moderation and runtime guardrails. Develop autonomous research frameworks using LLMs and agents to discover vulnerabilities, generate safety datasets, and propose alignment methods. Partner with research, model development, platform engineering, and data science teams to transition safety concepts from prototypes into production systems.

Own Cresta’s web platform, CMS architecture, component system, marketing instrumentation, localization, SEO/AEO tooling, and GTM integrations. Build systems that enable marketers to ship independently, while developing, deploying, evaluating, and optimizing AI agents for marketing use cases. Partner with product, engineering, design, and marketing teams to take AI agents from prototype to production, establish guardrails and human review processes, and influence the product roadmap.

Own Cresta’s marketing web platform, including architecture, CMS, component systems, analytics instrumentation, localization, SEO, and AEO infrastructure. Build tooling that enables marketers to ship independently and develop, deploy, evaluate, and optimize AI agents for marketing use cases. Partner with design, marketing operations, product, and engineering to deliver production systems, establish guardrails, and influence the AI platform roadmap.

Build and deploy production machine learning systems for personalized recommendations, fraud detection, risk scoring, ranking, classification, and anomaly detection. Own the full ML lifecycle, including mathematical formulation, feature engineering, training, evaluation, A/B testing, deployment, monitoring, and reliability. Develop low-latency real-time inference services using large-scale donor and transaction data, collaborate with Platform and DevOps teams, and mentor engineers on ML best practices and scientific rigor.

Design, build, and operate GiveCampus’s AI platform, including shared context and memory, identity and access controls, agent registries, governance, evaluations, monitoring, and security guardrails. Build reusable infrastructure and templates for internal AI builders, support agent orchestration and deployment, and measure business impact. Partner with product, engineering, and departmental AI teams while growing into the company’s technical AI lead.

Build developer tools for Modular’s MAX AI inference stack, including profiling, tracing, debugging, correctness validation, metrics, dashboards, and agent-native workflows. Integrate tooling across MAX, Mojo, driver APIs, and orchestration layers while supporting heterogeneous hardware. Collaborate with kernel, model, and serving engineers to improve developer productivity, performance analysis, and deployment health.

Build and own ClickUp’s AI evaluation systems, agent harness, self-improvement loops, model benchmarking, routing, and fallback strategies. Architect scalable backend and AI platform services for LLM deployment, orchestration, monitoring, logging, and automated evaluation. Improve reliability, privacy, security, and compliance while integrating AI features into the core product and collaborating cross-functionally.

Lead the architecture and implementation of security controls for LLM applications, RAG systems, autonomous agents, and AI-enabled engineering workflows. Build AI security testing, red-teaming, monitoring, guardrails, automation, and secure development patterns. Establish technical direction, threat-model complex systems, secure model and tool access, respond to agent incidents, evaluate emerging AI technologies, and mentor engineers across security and platform teams.