Top AI Jobs in Austin, TX

Lead customer-facing architecture for AI data platforms and AI Factory environments. Design data-centric solutions for RAG, inference, and training workflows; size GPU and storage environments; optimize data movement, retrieval, latency, throughput, cost, and reliability. Partner with customers, sales, and Product Management to qualify opportunities, influence product roadmaps, create reference architectures, and develop scalable go-to-market solutions.

Owns the backlog, sprint cadence, releases, and production health of enterprise AI products. Translates product strategy into requirements, user stories, work packages, and sprint goals while coordinating engineers, data scientists, business partners, and dependent teams. Leads Scrum ceremonies, PI planning, testing, release communications, and uptime monitoring. Applies AI tooling to improve delivery and uses technical fluency to prototype, review pull requests, and support responsible AI implementation.

Lead data science engagements with customers to acquire, clean, analyze, and model data; develop scalable operational models and evaluation methods; coach engineers on best practices; drive solution strategy, ethics, and industry thought leadership; write extensible code and collaborate across teams to deliver cloud-based AI solutions.

Lead hands-on engineering for an AI-powered email application across backend, mobile, and desktop platforms. Design reliable AI workflows involving orchestration, state, tool use, retries, fallbacks, and failure handling. Define APIs and abstractions, make architecture and scalability decisions, improve observability and reliability, and lead a small team of senior engineers through technical reviews, mentorship, and production delivery.

Design, develop, and implement AI/ML models; analyze large datasets; integrate AI solutions into existing systems; apply MavLink for autonomous systems; collaborate across teams and improve ML development processes.

Leads enterprise AI-native engineering initiatives by researching and prototyping AI tools, designing AI-enhanced workflows, creating reference architectures, and developing proofs of concept. Owns AI training and enablement, including documentation, presentations, tutorials, demonstrations, and coaching. Evaluates tooling, supports adoption, communicates technical changes to leadership, hosts engineering office hours, and establishes scalable standards across the organization.

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

Leads data science and marketing analytics engagements using AI, machine learning, behavioral data, CRM, CDP, transactional, digital, and survey data. Directs modeling across segmentation, propensity, churn, customer lifetime value, and engagement analytics. Translates complex findings into executive recommendations, leads client workstreams, supports business development, mentors analysts and data scientists, and partners with research, strategy, technology, and data engineering teams.

The Staff AI Engineer will analyze AI quality, implement improvements, manage projects, collaborate with teams, and shape AI capabilities in a remote-first environment.

Lead Engine’s internal AI Platform organization and technical strategy. Own the LLM gateway, agent frameworks, evaluation systems, developer-experience tooling, automation APIs, RAG and agent data infrastructure, observability, security, and cost controls. Build the initial platform hands-on, establish a unified enterprise AI stack, partner with Applied AI and Workforce Transformation leaders, and scale the organization through senior engineering leadership. Drive the Velocity program toward increased delivery autonomy and adoption across engineering.

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.

Develop and optimize GPU-accelerated generative AI training and inference workloads on NVIDIA platforms. Collaborate with developers, academia, and internal engineering teams to improve algorithms, frameworks, low-level libraries, hardware, and software performance. Analyze CPU and GPU architectures, contribute to open-source projects, and communicate innovations through technical publications and conferences.

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.

Leads organizational strategy, design patterns, tooling, and evaluation frameworks for prompts, agentic workflows, and LLM-based application architectures. Ships production LLM products, develops reusable systems using RAG, embeddings, vector databases, and agent frameworks, applies responsible AI principles, mentors engineers, and influences technical direction.

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.

Lead client-facing AI program governance and delivery as PMO manager. Translate business objectives into AI roadmaps and OKRs, oversee AI/ML lifecycle, ensure data readiness, model validation, deployment, and responsible AI compliance. Manage PMO standards, portfolio prioritization, AI-augmented reporting, and lead executive communications while mentoring PMs, TPMs, and analysts.

Lead go‑to‑market and close enterprise deals for AI/GenAI healthcare solutions across payers, providers, PBMs, and life sciences. Own revenue targets, shape solutions with architects and product teams, run POCs and demos, craft proposals and pricing, and present ROI/value to CXO stakeholders.

Builds and deploys AI-enabled applications, machine learning models, data pipelines, APIs, and decision-support tools for aggregate and asphalt operations. The role optimizes plant automation, production efficiency, uptime, quality, pricing, sales processes, and costs using operational, equipment, quality, GIS, and sales data. It integrates AI with plant systems and legacy software, collaborates across operations and IT, and ensures responsible AI, cybersecurity compliance, documentation, and user adoption.

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.

Evaluate AI model outputs, conduct audit-based reviews, identify patterns and failure modes, apply and improve evaluation guidelines, and document actionable findings. The role requires native-level Marathi, strong English, structured writing, GenAI safety knowledge, critical thinking, and comfort making judgment calls in ambiguous situations. Associates collaborate with leads and stakeholders, participate in calibrations, and help refine quality standards. This is a full-time, one-year, 100% onsite contract role in specified U.S. cities.

Lead design and delivery of an enterprise Generative AI platform across AWS Bedrock, Azure Foundry/OpenAI, and Databricks. Architect multi-cloud RAG/agent solutions, author IaC and Harness pipelines, enforce governance/FinOps/observability, mentor senior engineers, and enable data scientists and app teams to ship production GenAI at scale in regulated environments.

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.

Lead end-to-end product strategy and roadmap for AI-enabled platform capabilities. Define value proposition, target segments, and business case; translate market needs into product requirements; align stakeholders and drive cross-functional delivery, adoption, and go-to-market execution.