Top AI Jobs in Austin, TX

Design and deliver AI-powered fraud defenses, build cloud-native systems, collaborate across teams, and mentor engineers in a dynamic environment.

Lead and build Deepgram's AI enablement function: prototype and ship reusable agents, MCP servers, paved-road workflows, prompt and retrieval patterns, and an enablement hub. Drive company-wide AI adoption with measurable productivity metrics, embed safe-use guardrails, partner across Platform, Security, Data, and People Ops, and grow a central team and distributed champions network.

Lead end-to-end delivery of ML infrastructure and AI tooling programs, defining architecture and rollout strategies for training, serving, and monitoring. Coordinate research, engineering, product, and data teams to optimize inference cost/latency, build internal tools for experiment tracking and model versioning, and resolve bottlenecks to enable faster, scalable ML deployments.

Build and productionize applied AI/ML systems (predictive ordering and agentic copilots) end-to-end, drive measurable business KPI impact, collaborate with product/engineering, implement model-ops, observability, HITL workflows, and mentor peers.

Lead development and delivery of production-grade AI/GenAI solutions for health systems, architect RAG and agentic workflows, manage global healthcare data science teams, ensure HIPAA-compliant MLOps and model governance, engage executive health-system stakeholders, and translate clinical workflows into measurable AI use cases across clinical, financial, and operational domains.

Lead design, build, and operation of scalable, secure AI-enabled platforms for People Tech & AI. Mentor engineers, manage delivery, ensure reliability and security, collaborate cross-functionally, and implement conversational AI and cloud-native solutions.

Lead teams of data scientists and ML engineers to design and deploy AI solutions using Oracle tools and modern AI/ML practices. Manage clients, translate business problems into AI architectures, drive innovation, ensure quality, and mentor staff.

Develop, test, and validate generative AI agents and data pipelines, implement automated testing and governance controls, analyze complex problems, mentor junior associates, maintain high technical and professional standards, validate Power Automate integrations, and execute LLM evaluation frameworks to support AI-enabled client solutions.

Develop, test, and validate generative AI agents and data pipelines, implement automated testing and governance controls, analyze complex problems, mentor junior associates, ensure technical quality, execute LLM evaluation frameworks, and validate integrations (e.g., Power Automate) while collaborating with data scientists and ML engineers to deliver AI-enabled client solutions.

Lead development of AI and GenAI solutions for health systems, advise stakeholders, guide teams through ambiguity, ensure operational excellence and compliance, and implement MLOps, model governance, and production AI use cases against clinical data sources.

Design, implement, and deploy advanced AI/ML solutions using Oracle and modern ML tooling. Analyze complex problems, mentor junior staff, build client relationships, manage data pipelines and model lifecycle (CI/CD and monitoring), and contribute to cross-functional delivery while upholding professional and ethical standards.

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.

Lead and hands-on build the AI Gateway: set technical/product direction, implement core systems and developer tooling, recruit and mentor engineers, collaborate with platform teams, and ship high-velocity AI infrastructure and agent tooling.

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.

Design, build, and operate agentic AI systems end-to-end for Professional Services. Translate stakeholder needs into secure, scalable multi-agent architectures, define performance benchmarks, manage deployments and incident response, advise leadership, mentor teammates, and maintain runbooks and production tooling including on-call responsibilities.

The Principal AI/ML Engineer will develop machine learning models for campaign optimization in AdTech, collaborating across engineering and product teams to enhance advertising capabilities.

Lead and scale a cross-functional AI and engineering organization that builds production-grade AI models, automated data pipelines, and LLM-driven workflows to power Samsara's GTM operations. Define a multi-year AI roadmap, set quality and review standards for model outputs, collaborate with execs across Engineering, Product, and RevOps, and hire and mentor managers and individual contributors to drive adoption and reliable automation across CRM, telemetry, and enterprise data systems.

Hands-on technical leader who designs, builds, and deploys production-grade AI automation and agentic workflows. Responsibilities include rapid prototyping, RAG and document-intelligence systems, API integrations, orchestration, monitoring, reusable AI assets, and mentoring a small engineering team to deliver frequent releases and measurable business impact.

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.

Own design, implementation, and optimization of AI-powered support workflows across chat, voice, and email. Automate repetitive tasks, run pilots, monitor KPIs (CSAT, containment, deflection, productivity), establish quality standards, and partner cross-functionally with Product, Engineering, CX, Analytics, and external AI vendors to scale solutions.

Lead applied AI/ML initiatives, design and deliver production-grade software, develop secure high-quality code, troubleshoot, automate remediation, evaluate vendor architectures, champion best practices across engineering, and drive adoption of AI/ML, NLP, LLMs and streaming technologies.

Lead intake, roadmap, and governance for an AI automation/platform team. Harden and ship business-built AI apps to production with secure GCP platforms, define acceptance criteria and responsible-AI gates, manage vendor buy-vs-build decisions, drive adoption, measure outcomes, and manage AI economics and stakeholder communication.

Evaluate and validate AI Coach chat outputs for accuracy and risk, act as subject-matter expert in personal financial planning, identify enhancements, escalate issues to Ops/Product/Compliance, and contribute reusable solution libraries to improve member experience.