Top AI Jobs in Austin, TX

Lead client engagements to optimize manufacturing and product development lifecycles using lean and Six Sigma methods, supply chain integration, PLM, and digital technologies. Manage project planning, budgeting, team coaching, feasibility and cost analyses, and drive adoption of emerging technologies to improve speed to market, reduce cost, and enhance quality.

Consult with manufacturing clients to analyze processes, identify bottlenecks, and develop strategies to improve speed to market, reduce costs, and enhance quality. Implement lean and Six Sigma methodologies, optimize supply chain integration, leverage digital technologies, conduct feasibility and market analyses, manage project resources, and advise on compliance while building client relationships and developing team leadership skills.

Serve as a global SME for Industrial Metaverse technologies (PLCs, IoT, cloud/edge, digital twins, AI, EMC/RED, cybersecurity). Lead standards work, develop test programs, perform and supervise complex testing, train staff, represent UL externally, and support clients and continuous improvement across regional or global labs.

As a Staff Product Manager, you'll drive the strategy and success of SIEM products, collaborating with various teams to fulfill customer needs and market demands while focusing on security for SMBs.

Lead and operationalize GitLab's Internal Audit function: plan and execute risk-based audits, oversee SOX testing, facilitate enterprise risk management, coach a distributed audit team, implement QA/improvement programs, and report to senior leadership and the Audit Committee.

Lead privacy implementation and regulatory change for North America and LATAM. Partner with product, engineering, legal and risk to design privacy-forward products, advise on partnerships and licensing (including OCC), oversee regional compliance, audits, DPIAs, incident handling, and support AI governance and expansion into new jurisdictions.

Lead design and implementation of Microsoft Dynamics 365 solutions, supervise and mentor teams, manage project delivery, translate complex requirements into technical designs, handle data migrations and integrations, and build strong client relationships while improving service delivery.

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.

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.

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.

Lead design and evolution of customer-facing digital commerce systems (checkout, billing, payments, subscriptions, catalog, cart, orders). Use AI-assisted workflows to accelerate coding, review, testing, and documentation while ensuring security and production readiness. Drive architecture and API decisions across modern web apps, improve engineering excellence (testing, automation, observability, DevOps), build reusable patterns, and coach engineers to raise team productivity and technical delivery.

Lead product definition and lifecycle for Arm's cloud AI compute subsystem offerings. Translate partner and market needs into engineering priorities, support partner enablement from RTL to physical implementation, and align cross-functional teams on roadmap and execution.

Drive adoption of Atlassian's AI platform (Rovo) by discovering high-impact use cases, co-designing and building custom agents, configuring connectors, and deploying solutions to production. Run workshops, enable customers, collaborate with account and product teams, and measure adoption outcomes to expand AI use across enterprise workflows.

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.

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.

Lead design, build, and productionization of scalable, secure data pipelines and products in Azure Databricks. Implement batch and streaming ETL, Vector Search, RAG, LLM integrations, automation with Cursor/agents, CI/CD, observability, and governance. Mentor peers and partner with data scientists to operationalize AI while owning platform reliability and efficiency.

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.

The Principal Edge AI Firmware Engineer will develop and optimize embedded software for AI applications, focusing on performance and energy efficiency. Responsibilities include enhancing AI runtimes, profiling tools, and ensuring customer integration across embedded environments.

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.

The AI Engagement Manager will lead strategic relationships with institutional owners to harness AI, improve asset performance, and drive measurable ROI across real estate portfolios.

Design, implement, and deploy end-to-end ML models and data pipelines for ranking, recommendations, search, and personalization. Build scalable data quality checks, monitoring, and evaluation (A/B tests, offline/online) to ensure reliable, fair, production-grade AI. Collaborate with product, analytics, and engineering to translate business needs into ML solutions and drive iterative improvements.

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.