Top AI Jobs in Austin, TX

Build production-grade platforms and governance tooling for AI-assisted software development. Responsibilities include developing reusable standards, registries, APIs, developer tools, AI marketplaces, security and intellectual-property integrations, and CI/CD governance. The role partners across engineering and security teams, takes ambiguous problems from concept through production adoption, and measures platform impact, reliability, and developer experience.

Define and drive CrowdStrike’s company-wide AI and agentic architecture. Design production AI systems, shared platforms, lifecycle workflows, and developer tooling across products and business units. Improve performance, resilience, scalability, observability, governance, security, and auditability across cloud and edge environments. Build reference implementations, guide architectural decisions, resolve complex engineering challenges, mentor technical leaders, and align senior stakeholders around AI strategy and implementation.

Lead product vision and strategy for an end-to-end AI-powered software factory. Integrate planning, coding, review, and verification across GitLab Duo Agent Platform and product areas. Collaborate with engineering, design, and GTM teams, identify gaps and dependencies, present recommendations to executives, and drive execution to deliver cohesive developer workflows.

Leads AI and data solution architecture for client transformation programs. Designs generative AI, data modernization, LLM, vector database, RAG, and LLMOps solutions; develops roadmaps and evaluation frameworks; advises executives; drives innovation; manages workstreams, client relationships, business growth, thought leadership, and team mentorship.

Leads engineering teams delivering scalable AI and machine learning solutions on Google Cloud. Oversees generative AI architecture, Vertex AI, LangChain, LangGraph, LLM APIs, RAG, experimentation, evaluation, guardrails, and LLMOps. Guides technical strategy, client alignment, delivery execution, and team development while applying Python, data science, and machine learning methods to build reliable AI products.

Leads Google Cloud AI and machine learning engineering teams and client workstreams. Oversees generative AI, LLM application, agent orchestration, retrieval-augmented generation, model evaluation, prompt engineering, guardrails, and LLMOps initiatives. Reviews architecture, code quality, testing, and solution delivery using Vertex AI, LangChain, LangGraph, Python, and LLM APIs. Coaches team members, improves reusable delivery assets, and manages client relationships, timelines, quality, and expectations.

Leads client-focused generative AI and data modernization architecture, translating business needs into scalable solutions using Google Cloud, Vertex AI, LLMs, vector databases, and modern data platforms. Advises stakeholders, develops AI evaluation and guardrail practices, guides experimentation and deployment, presents technical recommendations, supports proposals, and coaches teams through complex transformation programs.

Lead architecture and hands-on development of reusable customer-facing AI platform services, including conversational AI, agent orchestration, retrieval, grounding, memory, tool integration, and policy enforcement. Define APIs, data models, deployment designs, testing, evaluation, and operational requirements. Build production systems across GCP and Azure while driving reliability, security, observability, CI/CD, and technical standards. Provide staff-level influence through architecture reviews, mentoring, code reviews, and cross-functional leadership without direct reports.

Design, build, deploy, and manage AI systems and automations that improve customer onboarding, support, engagement, retention, and expansion. Develop predictive customer insights, digital experiences, workflows, and integrations with Salesforce, support systems, and data infrastructure. Translate ambiguous Customer Experience requirements into scalable technical solutions, independently make architecture decisions, monitor production systems, measure adoption and ROI, and continuously optimize based on user feedback and changing business needs.

Leads AI strategy and ecosystem solutions for Fortune 500 clients, designing agentic workflows, multi-agent architectures, and AI-native storage solutions. Partners with executives and internal Product, Engineering, and GTM teams to translate business challenges into scalable technical blueprints. Evaluates LLM, RAG, and AI agent trends, develops ROI models and proof-of-concepts, and shapes enterprise platform strategy while building repeatable solutions and team capabilities.

Evaluate AI model outputs, audit data and model behavior, identify edge cases and failure modes, apply and improve evaluation guidelines, communicate performance trends, participate in calibration discussions, and document actionable findings. The role requires native-level Tamil, strong English and writing skills, critical judgment, attention to detail, and familiarity with GenAI safety considerations. This is a full-time, one-year, 100% onsite contract position.

Build AI-powered workflows, agents, integrations, and internal tools that automate People Operations. Partner with People, Engineering, and IT to modernize onboarding and other employee processes, while implementing secure access controls, human oversight, escalation paths, and production-ready architectures. Prototype with users, iterate quickly, and measure improvements in efficiency, employee experience, and operational consistency.

Lead GitLab’s company-wide strategy for autonomous, agentic software development workflows. Identify and validate AI opportunities across the SDLC, build prototypes and reference architectures, establish evaluation frameworks and safety controls, and drive internal adoption. Partner across Engineering, Product, Infrastructure, Security, Architecture, and Data and ML teams to scale reliable, observable, compliant agentic capabilities into customer-facing products. Mentor senior technical leaders and represent GitLab externally on AI-assisted development and productivity measurement.

Build and deploy AI agents and integrations across the go-to-market application stack. Responsibilities include configuring GTM systems, developing Salesforce data pipelines, identifying automation opportunities, engineering prompts, monitoring agent performance, implementing human-in-the-loop controls, troubleshooting workflows, and evaluating new AI tools. The role partners with Revenue Operations, Sales, Customer Success, and Salesforce teams to deliver scalable AI-augmented solutions.

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.

Build and own AI safety systems for Realtor.com’s LLM-powered real-estate assistant. Responsibilities include developing Fair Housing and content-moderation classifiers, creating evaluation datasets and pipelines, measuring precision, recall, F1, and confusion matrices, integrating cloud guardrails, red-teaming for jailbreaks and prompt injection, managing Terraform infrastructure, and partnering with engineering, product, legal, and compliance teams to launch safe AI capabilities.

Architect and operate Realtor.com’s internal AI platform, including agentic architecture, APIs, SDKs, LLM gateways, governance, identity controls, observability, and AWS infrastructure. Manage AI tool lifecycles, access, licensing, maintenance, support, and runbooks for platforms such as Portkey, Claude, Gemini, Devin, and Glean. Establish protocols, guardrails, secure deployment standards, and scalable paved paths while partnering across engineering, Security, IT, Legal, and business teams.

Designs, prototypes, deploys, and maintains AI copilots, workflow agents, and low-code applications. The role includes stakeholder discovery, workflow mapping, cloud-based development, API and data integration, production deployment, monitoring, prompt and retrieval optimization, and lifecycle ownership. The engineer will also teach AI best practices through workshops, documentation, labs, and office hours while measuring adoption and iterating on solutions.

Manage enterprise AI platforms, including configuration, licensing, capacity, agent deployment, user onboarding, governance, security, compliance, monitoring, lifecycle management, and cost tracking. Partner with developers, architects, cybersecurity, legal, privacy, HR, and business teams to operate AI tools reliably. Establish standards for AI application lifecycle management, source control, deployment automation, release processes, access controls, and operational documentation.

Develop and implement AI solutions and machine learning systems that enhance products and solve complex client problems. Responsibilities include advanced analytics, LLM production deployment, prompt engineering, RAG pipeline optimization, cross-functional collaboration, technical discovery, responsible AI advocacy, client relationship building, and mentoring junior team members.

Design and deploy production AI systems that transform large-scale geospatial data into actionable intelligence. Build end-to-end ML pipelines, multimodal and reasoning models, experimentation frameworks, synthetic datasets, and scalable inference systems. Collaborate with research, engineering, product, and domain teams while optimizing models for reliability, latency, and cost. Support monitoring, retraining, evaluation, and next-generation Earth AI capabilities.

As Director of AI Change Enablement at eBay, you will lead the AI transformation strategy, drive change management, and ensure employee adoption of AI initiatives across the enterprise, acting as a strategic partner to leadership.

Lead complex applied-AI technology programs from initiation through delivery and support. Define scope, plans, governance, risks, and dependencies; coordinate engineering, QA, product, and operations; use enterprise AI capabilities responsibly to accelerate planning and reporting; ensure production readiness and continuous improvement.

Develops, configures, tests, and supports AI solutions, workflows, integrations, and model-enabled applications. Creates technical instructions and documentation, troubleshoots and optimizes AI systems, coordinates technical tasks, and collaborates with architects, delivery leaders, project teams, and stakeholders. Requires a college degree or equivalent, three years of related experience, technical communication skills, and the ability to travel and participate in on-call availability.

Own strategic customer relationships and lead deployment of AI workflows into supplier operations. Map customer SOPs and data sources, identify high-ROI AI opportunities, translate needs into product and implementation requirements, drive adoption, partner with product/engineering, build playbooks, track value, and surface expansion opportunities across global teams.