Top AI Engineer Jobs in Austin, TX

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.

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.

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.

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, build, deploy, and maintain resilient, scalable backend services and platform components for Toast's Voice AI platform. Lead cross-cutting architecture and projects, integrate AI platforms (LLMs), implement observability and evaluation for AI systems, set engineering standards, and mentor engineers.

Lead and coach teams to design, develop, and deliver production-grade AI and data solutions for clients. Manage client accounts, ensure quality and timelines, analyze complex datasets, implement best practices, and drive continuous improvement while fostering client relationships and technical excellence.

Own production operations for workplace AI platforms, including configuration, integrations, observability, secrets, access controls, model and prompt changes, benchmarking, and incident response. Translate technical findings into governance, risk, compliance, and training materials while collaborating with Security, Legal, HR, and business stakeholders. Investigate failures, implement mitigations, document root causes, and maintain reliable, secure AI services.

Develop and operate production ML and AI systems powering patient experiences such as matching, ranking, recommendations, onboarding, and personalization. Build reusable ML infrastructure, pipelines, evaluation, observability, and model-serving capabilities. Provide technical leadership across the ML lifecycle, guide architecture, evaluate custom versus foundation-model approaches, and partner with ML and Patient Engineering teams to deliver reliable, scalable AI products.

Design and implement AI agents, plugins, automations, and enterprise integrations for HR Technology platforms. Lead requirements gathering, technical analysis, solution design, testing, documentation, deployment, security, and data integrity efforts across HR, IT, and business stakeholders. Integrate platforms including ServiceNow, Workday, and other enterprise applications while supporting the HR automation and AI roadmap.

Build and manage AI-powered workflows, agents, automations, and internal applications for Deal Desk and GTM teams. Identify automation opportunities, develop prototypes, scale solutions to production, integrate enterprise data and APIs, implement human validation and audit controls, optimize LLM costs, document tools, measure adoption, and train users. The role requires Deal Desk or RevOps experience plus practical knowledge of AI models, APIs, scripting, SQL/SOQL, Python, CPQ, CLM, Salesforce, and agentic platforms.

Design, develop, deploy, and support AI solutions including generative AI applications, agents, workflow automation, document intelligence, and retrieval-augmented generation. Partner with business and IT teams to identify use cases, integrate enterprise platforms and data sources, establish prompt and evaluation standards, support AI governance and security, monitor solution performance, and train employees on responsible AI adoption.

Build control-plane and telemetry experiences for AI agents, translating distributed-system concepts into understandable products. Design APIs, domain models, workflows, visualizations, human-oversight patterns, auditability, and recovery mechanisms. Work across backend and frontend systems while shaping architecture, product direction, information design, and AI-native interactions. Apply sound judgment to determine when agents, humans, graphical interfaces, APIs, or guardrails should control actions.

Designs, trains, evaluates, and deploys advanced machine learning systems from research prototypes to scalable production pipelines. Responsibilities include deep learning experimentation, model fine-tuning, distributed and mixed-precision training, production observability, safeguards, research evaluation, and continuous improvement. The role also explores large language models, retrieval-augmented generation, agentic systems, multimodal architectures, and responsible AI practices while delivering impactful applied AI solutions.

Develop core libraries and tools for scalable agentic applications. Responsibilities include optimizing agent frameworks and harnesses, benchmarking performance, improving latency and cost, integrating RAG systems and vector databases, supporting GPU workflows, designing cross-language APIs, debugging runtime internals, and collaborating with open-source communities. The role also involves building evaluations, observability, middleware, guardrails, and other systems that improve agent quality, developer productivity, and deployment reliability.

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.

Lead AI infrastructure projects, develop GenAI solutions, drive automation strategy, and collaborate with IT and business teams for implementation.

Build AI-powered finance applications using Databricks, Python, LLMs, and governed financial data. Develop tools for variance commentary, financial close checklists, reconciliation, FP&A, natural-language data exploration, and financial analysis. Partner with finance stakeholders, measure adoption and productivity gains, and maintain governance separating AI recommendations from financial controls. The role requires software engineering, finance process knowledge, and experience with Databricks, LLM applications, finance systems, and production-ready applications.

Build and operate large-scale data systems supporting AI training and evaluation pipelines. Responsibilities include data ingestion, transformation, quality assurance, lineage, versioning, reproducibility, and high-throughput delivery for multimodal and accelerator-based workloads. The role requires operating petabyte-scale infrastructure, applying distributed systems and software engineering practices, and collaborating cross-functionally to improve model quality and training efficiency.

Build and ship full-stack software and LLM-powered features for student records, tutoring, parent portals, coaching dashboards, and school operations. The role includes end-to-end ownership of smaller systems, AI-assisted development, prompt and agent workflows, retrieval over student data, evaluation design, and regression testing. Candidates collaborate directly with founders and senior engineers in a fast-moving Austin-based environment.

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.

Design, build, and maintain infrastructure software for large-scale AI training and inference. Improve efficiency, resiliency, availability, observability, and reliability across distributed AI systems. Develop APIs and tools, analyze failures from application through hardware levels, define reliability metrics, and support NVIDIA AI platforms. The role requires expertise in distributed systems, monitoring, debugging, software engineering practices, and AI infrastructure, with preferred experience in large clusters, RDMA stacks, telemetry, and deep learning frameworks.