Top AI Automation Jobs in Austin, TX

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.

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.

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.

Design, build, and productionize machine learning models to attribute cloud costs, forecast usage, and detect anomalies. Analyze large-scale cloud and financial datasets, run experiments, collaborate with engineers to deploy and monitor models, and report project status while improving team technical capability.

Lead end-to-end design of AI-enabled networking experiences: convert ambiguous AI opportunities into product concepts, prototypes, and shipped features. Define AI-first interactions, data and telemetry requirements, reusable UX patterns for control and explainability, and partner with platform, engineering, and research teams to deliver trustworthy, measurable intelligence features.

Build end-to-end internal AI agents and workflow automations: discover high-value opportunities, redesign processes, implement LLM-based agents with integrations, deploy and operate solutions, and ensure security, compliance, monitoring, and adoption across GTM teams.

Lead client engagements in insurance data, analytics, and AI: define roadmaps, run discovery sessions, design solutions, manage delivery and teams, develop proposals, and ensure DataOps, cloud (AWS), governance, and best practices across AI and analytics initiatives.

Enable and mentor partners to sell and deliver NiCE AI/CCaaS solutions. Lead technical discovery, demos, PoVs, and architecture reviews; build partner enablement content; manage technical partner relationships, integrations, release impacts, and service scoping.

The Principal Consultant leads AI and digital transformation engagements, guides clients in identifying AI opportunities, builds strategies and roadmaps, implements solutions, and develops teams to achieve measurable business outcomes.

The role leads AI adoption across business functions, prioritizing use cases, building business cases, and driving organizational change necessary for successful implementation. It requires collaboration with IT leadership and the presentation of ROI to business stakeholders.

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.

Own and administer Salesforce and NetSuite while designing and deploying production-grade AI-driven automations and agent workflows. Ensure data governance, PHI/HIPAA compliance, release management, and quote-to-cash integrity. Partner cross-functionally to prioritize work, coach teams, document systems, and measure impact of automation on operations and revenue.

Build and ship AI-native systems for a fintech app: AI agents, RAG/retrieval systems, tool-calling and automation for onboarding, KYC, support, risk, CRM and operations. Work with product and engineering to design reliable, low-hallucination, cost- and latency-aware solutions and turn manual processes into scalable AI-enabled systems.

The SME will develop a curriculum on AI for finance, creating engaging content and videos to train finance professionals in AI applications. They must leverage their extensive finance experience and AI understanding to enhance learner success.

The GTM AI Enablement Lead will unify go-to-market strategies, design training, improve processes through AI workflows, and enhance customer experience.

Lead strategic customer co-innovation engagements for AI and autonomy. Manage executive briefings, define/govern co-innovation frameworks, track milestones and commercial impact, capture insights for productization and sales enablement, and convert outcomes into event and marketing content.

Lead development of AI-driven solutions for timing and constraint analysis in EDA, integrating data sources and creating autonomous analysis agents.

Build and deploy production micro-apps and LLM-powered agents on a governed GCP platform. Integrate AI with systems (Salesforce, Slack, NetSuite, SQL), harden apps with auth, secrets, CI/CD, logging and monitoring, automate maintenance, and implement evaluation and observability for model quality and drift.

Contribute to building and hardening micro-apps and AI integrations on a shared GCP platform. Complete bug fixes and small features, participate in code and design reviews, apply security and software development best practices, monitor applications and follow runbooks, and use AI tools to assist development, testing, and documentation while learning from senior engineers.

Design and architect AI and agent-based systems for industrial automation, develop proofs of concept, collaborate with customers and cross-functional teams, and support productization and standards while following coding/testing/debugging practices.

Develop and lead multimodal Vision-Language-Action (VLA) systems for industrial applications: design, fine-tune, and evaluate multimodal foundation models and agentic AI, integrate perception with control systems, and translate research prototypes into deployable solutions.

Develop and prototype AI/ML-driven automation for ASIC/SoC design and implementation. Collaborate with academia and cross-functional NXP teams to apply GenAI, RL, GNNs, and CNNs to VLSI CAD flows, pilot solutions on real designs, and improve power, performance, area, and quality.

Support AI project teams by executing workstreams, preparing client-facing presentations and documentation, assisting implementation activities, maintaining required certifications, using relevant technology, communicating with stakeholders, and traveling as needed for client engagements.

Lead design and delivery of agentic AI systems and LLMOps on AWS to generate, validate, and deploy infrastructure-as-code (Terraform). Build multi-agent applications, RAG pipelines, and AIOps for cloud operations; integrate AI into CI/CD and ITSM. Set technical direction, establish guardrails/policy-as-code, curate reusable Terraform modules, and mentor engineering teams.

Build and ship AI-driven automation across the go-to-market lifecycle: prospecting, lead routing, deal support, retention, and instrumentation. Design agents and workflows, connect GTM systems and data, deploy and monitor production AI, set guardrails and evals, and partner with Sales, Solutions Architecture, Marketing, and RevOps to measure and improve impact.