Top AI Jobs in Austin, TX

As a Senior Lead AI Solutions Consultant, you'll drive revenue by promoting the BrazeAI portfolio, leading technical discussions, and conducting AI Academy workshops, while collaborating across teams to enhance Braze's offerings.

The role involves developing, testing, and validating Generative AI agents and maintaining automated testing standards. Responsibilities include mentoring junior associates, analyzing complex issues, and applying governance controls in AI-driven solutions.

As an AI Engineering Manager at PwC, you will lead the design and operation of AI-powered platforms, mentor engineers, and ensure project delivery excellence while focusing on security and scalability.

Bridge model creators and developers by integrating external models, building demos and reference apps, authoring technical content, supporting developer communities, and influencing product and API roadmaps through partner and community feedback. Represent Cloudflare at events and provide technical support for model integrations.

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.

Use advanced analytics, statistical techniques, and machine learning to extract insights from large datasets. Design data solutions and pipelines, build predictive models (R, MATLAB), and apply TensorFlow and Scikit-Learn for deep learning and NLP. Collaborate with teams and clients, deliver quality work, and adhere to firm standards.

Lead AI agent build and DevOps workstreams within a Data & Analytics practice. Develop and implement BI and cloud analytics solutions, build visualizations (QlikView, Spotfire), validate data integrity, manage client relationships, identify optimization opportunities, and mentor teams to ensure project delivery and performance.

Lead managed-services teams to design and deliver AI agent builds and DevOps-enabled analytics solutions. Develop data architecture, advanced analytics and visualizations, ensure data integrity/security, manage client accounts, mentor staff, and drive continuous improvement to generate strategic insights and business value.

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

Lead the development of innovative AI solutions, guide teams through complex challenges, and collaborate with diverse stakeholders to enhance operational effectiveness and achieve strategic objectives.

Manage AI engineering teams and client relationships, drive project deliverables, mentor staff, and uphold quality standards while leveraging advanced technologies.

Lead the creation of innovative AI solutions for healthcare systems, serve as a trusted advisor, foster collaboration, and drive operational excellence while managing complex challenges in healthcare AI.

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.

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

Lead architecture and execution of enterprise AI platform capabilities including scalable RAG pipelines, multi-agent orchestration, model routing, APIs, and observability. Drive infrastructure decisions (Azure, AKS, vector DBs), establish LLM integration patterns and guardrails, mentor engineering teams, and translate product priorities into production-ready AI platform services.

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.

Lead design and delivery of scalable, secure cloud-native data platforms and large-scale data pipelines. Enable AI/ML use cases via integrated data and model-serving workflows, enforce data quality, observability, and security, and mentor engineers while aligning technical solutions to business priorities.

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.

The Principal AI Compiler Engineer designs hardware for AI, ensuring models translate effectively to silicon and perform optimally in 5G applications.

The Principal AI Model Architect's role includes optimizing model architectures for new hardware, translating AI algorithms into hardware specifications, and projecting performance for silicon design.

The AI Platform Architect will design a cohesive architecture for AI environments, oversee workload orchestration, eliminate system bottlenecks, and collaborate on hardware-software integration. Responsibilities include developing a 3-to-5-year technical vision for the AI platform and ensuring data flow between AI compute nodes and network fabrics is optimized.

Embed with teams to audit workflows, build and productionize LLM/agent automations (using Claude and integrations), create reusable assets and guardrails, coach teams to self-sufficiency, and measure outcomes to prioritize future engagements.

The Advisory Agentic Technologist will be the Subject Matter Expert in identity management, driving technical success and strategy, collaborating on agentic AI innovations, and influencing product development, with a focus on the US, Canada, and Latin America.

Design, build, and ship production-grade agentic AI workflows using LangGraph, Temporal, and Pydantic. Implement AI observability with LangFuse, set AI engineering standards (RAG, prompt management, tool-calling), benchmark LLMs, deploy cloud AI (containerization, cost management), and mentor engineers while supporting enterprise SLA, security, and compliance needs.