Top AI Engineer Jobs in Austin, TX

Own and evolve Sonar's AI-native internal platform: design, build, and operationalize agent/connector integrations (Claude/MCP, Rovo), administer and productize Jira Service Management and Atlassian Cloud, implement scalable automations and integrations, maintain CMDB/assets, enforce ITSM/ITIL processes, support security/compliance audits, and partner across teams to drive platform roadmap and stakeholder enablement.

Architect, build, and operate an internal AI platform that enables agentic use cases and safe model access. Own platform APIs, SDKs, LLM gateway, provisioning, access governance, license hygiene, observability, and runbooks. Implement AI protocols (MCP, A2A, ARD), enforce identity and policy-as-code controls, strengthen AWS infrastructure (Bedrock, IAM, network security), and partner cross-functionally to drive platform standards and operational excellence.

Lead end-to-end development and scaling of AI/LLM solutions for marketing operations, including RAG applications, copilots, automation, production deployment, monitoring, and governance. Partner with cross-functional teams to build reusable frameworks, demonstrate measurable impact, and drive enterprise AI adoption.

Design and deliver enterprise-grade AI workflow transformations on GCP. Build secure, event-driven backend services and multi-step AI agents, integrate bots with enterprise systems, implement telemetry, testing, CI/CD, HITL validation, and create reusable architecture, documentation, and runbooks while guiding adoption and governance.

Design, build, and scale AI-enabled workflow and automation solutions across collaboration platforms and enterprise systems. Translate manual processes into secure, reusable, event-driven automations using Python/JavaScript, integrate AI capabilities like Gemini, implement logging and telemetry, and partner with product, security, and business teams to deploy and sustain enterprise transformation solutions.

Lead design and delivery of production-grade AI capabilities to improve developer productivity across engineering tools, CI/CD, and embedded software workflows. Define technical vision, integrate AI into toolchains, drive productionization, establish responsible AI standards, mentor engineers, and influence cross-functional roadmaps and implementations.

Design, build, and operate low-latency, cloud-native AI-powered fraud decisioning and automated remediation systems. Implement AI-assisted SDLC and agentic multi-agent workflows, modernize streaming/data pipelines and microservices, ensure observability/SLOs/security, evaluate vendor solutions, and mentor engineers.

Build production AI-powered analytics and natural-language intelligence on governed telematics, aftersales, and energy data. Design multi-agent systems, retrieval/grounding/reasoning/memory patterns, and reusable AI services. Partner with data product teams to ensure trust, explainability, scalability, and performance of AI experiences for enterprise users.

Design and build a global, serverless AI inference platform running on heterogeneous GPUs and accelerators. Optimize model scheduling, request routing, caching, and load balancing for low latency. Improve reliability and observability, lead cross-functional projects, and mentor junior engineers.

Lead and define AI architecture, ensuring scalable, reliable AI systems while mentoring engineers and collaborating with product teams.

As a Senior AI/ML Engineer, you'll enhance developer productivity by leading CI solutions, mentoring engineers, and implementing scalable systems.

The role involves architecting AI applications, delivering scalable solutions, conducting research, and implementing high-quality code for cloud platforms. Requires collaboration and effective communication.

Design and implement scalable ML infrastructure solutions, mentor junior engineers, own technical projects, and collaborate across teams to solve complex AI/ML problems.

The Staff AI/ML Engineer will design and implement scalable ML infrastructure solutions, lead projects, mentor peers, and enhance ML productivity at GM.

Design and build AI-native, high-performance distributed backend platforms for IT/OT/cybersecurity. Integrate LLMs and ML into production workflows, develop data pipelines and real-time processing, mentor engineers, and drive architectural direction and AI-assisted engineering practices.

Build and operate AI-driven People Ops systems on the Cloudflare developer platform: ship production workflows for hiring, onboarding, recruiting automation, self-service, AI copilots, data reconciliation, observable audit pipelines, and custom interview tooling. Integrate systems securely, implement authorization/audit logging, and work cross-functionally with HR, Legal, IT, and Security.

Design and deliver LLM-powered, agentic AI systems to automate customer support and compliance. Build scalable, secure backend infrastructure in Python and Go, integrate third-party models/APIs, implement grounding and guardrails, and measure performance. Lead end-to-end projects, mentor engineers, conduct code reviews, and participate in on-call and incident response to improve resolution times and reduce operational costs.

Design and build AI Gateway systems for high-volume, latency-sensitive inference at the edge. Implement APIs, routing, observability, controls, developer workflows and tooling. Prototype ambiguous ideas to production, improve reliability, performance, cost-efficiency and debuggability, and mentor engineers while dogfooding AI-native tools, agents, and automation to accelerate development and operations.

Design, build, and deploy production-grade ML systems for ICS/xOT cybersecurity, including threat detection, anomaly detection, NLP/LLM applications, and scalable data pipelines with observability and MLOps practices for cloud and on-prem environments.

Integrate and fine-tune existing AI models into production features, build evaluation frameworks and observability for AI performance, collaborate with data science and engineering, develop AI-powered features in Python, and troubleshoot and monitor models in real-world scenarios.

Design, prototype, and productionize generative AI features and agentic workflows. Build Python services, TypeScript/React UIs, data pipelines, evaluation and instrumentation, and safeguards. Partner with product and stakeholders to measure impact, ensure reliability, and mentor engineers.

As a Staff Machine Learning Engineer, you will design AI products on Edge devices, optimize ML model performance, and work with large-scale datasets, collaborating across teams to deliver impactful solutions.

Lead design and build of an AI-native marketing platform: integrations, multi-agent orchestration, UIs, tooling, analytics, CI/CD, auth/multi-tenancy, and production AI agents to automate marketing workflows and drive adoption across teams.

The Applied AI Engineer will build and maintain AI agents, manage integrations and monitor performance, ensuring quality and continuous improvement of AI tools at Zello.

Embed with strategic enterprise customers to prototype, build, and productionize full-stack generative AI and agentic applications; advise on AI strategy, codify repeatable solution patterns, mentor teams, and channel field feedback to product. Focus on scalable, secure AI architectures and LLM/MLOps practices to operationalize RAG, fine-tuning, semantic search, and agentic workflows.