Top AI 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.

Design, build, and operate low-latency, AI-powered fraud decisioning and automated remediation systems. Ship and maintain LLM and multi-agent applications, apply AI-assisted SDLC practices, implement observability/SLOs, simplify platform components, and mentor engineers while collaborating with product, security, and ops teams.

Lead design and deployment of enterprise-scale multi-agent AI systems across the semiconductor lifecycle. Define strategy, architect agentic AI platforms (LLMs, RAG, knowledge graphs), identify automation opportunities, establish AI governance, integrate with EDA and operations systems, mentor technical leaders, and represent the company externally.

Lead the identification, design, and delivery of AI-driven automation within Customer Operations. Translate operational pain points into scalable solutions, evaluate tools, run end-to-end implementations, partner cross-functionally, measure outcomes, and enable adoption to improve efficiency and customer experience.

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.

As a Sr. AI Solutions Engineer, you will develop AI solutions, prototype applications, teach teams, and drive AI adoption across the company.

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.

Lead the end-to-end AI-powered client activation experience, owning strategy, roadmap, and platform capabilities (SSO, APIs, data provisioning). Reduce time-to-value through intelligent onboarding, predictive models, and activation analytics while partnering across Engineering, Data Science, GTM, and Customer Success to instrument funnels and drive measurable activation outcomes.

Lead enterprise AI transformation and architecture engagements from strategy through delivery. Advise executives on AI roadmaps, design end-to-end AI and data architectures (including knowledge graphs, data catalogs, RAG, and integrations), establish governance and responsible AI practices, drive adoption and change management, and develop reusable practice assets and thought leadership to realize business value.

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

Design, build, and operate AI agents and decision-support systems embedded in GTM workflows. Conduct stakeholder research, prioritize problems, ship and iterate on agent outputs, monitor adoption and quality, and scale reusable solutions across sales and customer-success motions while staying closely embedded with field teams.

Senior individual contributor who defines enterprise AI architecture, builds reusable platform components and governance controls, drives observability and cost attribution, mentors engineers, and partners across Security, Legal, Data, and product teams to enable secure, measurable AI adoption.

Build and operate production AI capabilities—agents, prompts, connectors, automations, and telemetry—to improve productivity. Deliver reusable components, integrate AI with enterprise systems, enforce responsible AI controls, instrument costs and ROI, and collaborate across security, legal, data, and business teams to scale safe, measurable AI adoption.

Hands-on individual contributor building and operating enterprise AI capabilities: agents, prompts, connectors, automations, and telemetry. Partners with stakeholders to convert workflows into production-quality, secure, measurable AI solutions, create reusable components, enforce governance controls, optimize cost and ROI, and enable cross-functional adoption and observability.

As an AI Engineer, you will build and scale AI business applications, focusing on generative AI. Responsibilities include rapid prototyping, applying software engineering best practices, and providing production support.

Design, build, test, deploy, monitor, and maintain production LLM-based agentic solutions for Commercial Customer Success. Integrate agents with CRMs and Samsara systems, define evaluation frameworks and success metrics, implement automated tests and monitoring, iterate on models and prompts, and demonstrate impact on customer outcomes and retention.

Lead discovery and delivery of AI-first AEC product capabilities. Define roadmap, prototype agentic LLM workflows, translate research into requirements, measure outcomes, and partner with UX, Engineering, and GTM to drive adoption and growth.

Lead design and delivery of the AI-powered creative operating system connecting audience intelligence, briefs, concepting, asset production, and performance feedback. Build and standardize video and image AI pipelines, Figma automation, tool stack selection, and workflows that scale creative output. Embed with creative teams to ship production-ready systems, evaluate emerging capabilities, and ensure measurable increases in output with flat headcount.

Lead end-to-end performance of customer-facing conversational AI and knowledge base. Design, test, and optimize AI conversations, define intents and success metrics, analyze KPIs, maintain enterprise knowledge, run continuous improvement using analytics and experimentation, and partner cross-functionally to improve CX and business outcomes.

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.

The role involves researching, designing, and prototyping generative AI solutions for cybersecurity. Responsibilities include building datasets, training models, and presenting findings to various audiences.

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.

Build and deploy production micro-apps and LLM-powered agents on a governed GCP platform. Integrate AI with systems (Salesforce, Slack, NetSuite, SQL), harden apps with auth, secrets, CI/CD, logging and monitoring, automate maintenance, and implement evaluation and observability for model quality and drift.

Contribute to building and hardening micro-apps and AI integrations on a shared GCP platform. Complete bug fixes and small features, participate in code and design reviews, apply security and software development best practices, monitor applications and follow runbooks, and use AI tools to assist development, testing, and documentation while learning from senior engineers.