Top AI Engineer Jobs in Austin, TX

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.

Lead development of Avride’s ML infrastructure, initially owning GPU inference performance for onboard and simulation workloads. Build and optimize C++ execution frameworks, runtimes, and distributed infrastructure supporting neural network pipelines. Collaborate with applied ML teams on model architecture, diagnose performance issues, and expand ownership across broader ML pipelines. The role requires deep GPU and PyTorch knowledge, distributed systems experience, and strong expertise in multithreaded C++ environments.

Develops AI-assisted CAD/EDA automation, tools, and workflows for RTL design and verification. Responsibilities include troubleshooting EDA tools, building simulation and regression automation, improving CI/CD, analyzing infrastructure performance, supporting emulation and static sign-off, and collaborating with design, verification, infrastructure, cloud, and EDA vendor teams.

Architects and develops AI-powered systems, tools, agents, backend services, APIs, and experimentation frameworks using foundational AI models. Integrates AI capabilities with microservices, AWS, Snowflake, and complex data systems while ensuring scalability, reliability, and maintainability. Translates business problems into technical solutions, prototypes AI applications, collaborates with product and engineering teams, evaluates emerging AI technologies, and documents system designs and integration patterns.

Build and ship production AI systems across retrieval, knowledge graphs, inference, agentic workflows, and data platforms. Develop customer-context retrieval, scored signals, evaluation and observability capabilities, and workflows that turn insights into action. Work hands-on with large and small models, durable orchestration, cloud deployment, inference-cost optimization, and high-volume observability data. Establish reusable engineering patterns, collaborate with product and platform teams, and mentor through strong technical contributions.

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.

Build and productionize agentic workflows, RAG pipelines, AI services, backend systems, and ML pipelines for sales automation. Integrate Salesforce, quoting engines, data lakes, and internal platforms using scalable, event-driven architectures. Lead 0-to-1 development of tools for account research, lead prioritization, and quoting while ensuring reliability, observability, performance, and resilience across the GTM technology stack.

Founding Engineer responsible for building FilmoreAI’s production AI and data platform for construction equipment dealers. The role covers agent infrastructure, model routing, LLM extraction and reasoning, canonical data modeling, entity resolution, Python data pipelines, Azure cloud orchestration, evaluation, observability, and safe human-approved write-back. The engineer will work independently in an ambiguous startup environment and use agentic development tools to ship reliable systems with strong cost, latency, accuracy, and operational guardrails.

The Applied AI Engineer will build and maintain AI agents, manage integrations and monitor performance, ensuring quality and continuous improvement of AI tools at Zello.

Design and build a production AI agent platform with RAG pipelines, document parsing, structured data mapping, and multiple agent types. Deploy and operate ML models on GPU infrastructure while ensuring scalability, security, compliance, observability, and performance in a multi-tenant cloud environment. Collaborate with Product Management and engineering teams, provide technical leadership through design and code reviews, and mentor engineers.

Build production AI-powered software for hiring, finance, capital planning, contract analysis, and operational workflows. Develop LLM features, MCP servers, and agentic systems; embed with finance, legal, recruiting, accounting, and operations teams; translate expert workflows into structured data and automated processes; and ship autonomous, user-friendly products rapidly.

Build AI-powered software automating hiring, finance, capital planning, contract analysis, and operational workflows. Develop production features using LLM APIs, MCP servers, and agentic frameworks; integrate enterprise systems; transform expert processes into structured data and generated work; and collaborate directly with finance, legal, recruiting, accounting, and delivery teams. The role emphasizes autonomy, rapid execution, product judgment, and forward deployment with business experts.

Researches, develops, tests, and maintains materials-aware machine learning models and software. Designs scalable ML systems, improves model interpretability and uncertainty quantification, and develops inverse-design capabilities. Collaborates with product, engineering, and external research teams to deliver customer-focused AI solutions, publishes research, reviews code, analyzes model performance, and mentors developers.

Lead end-to-end client AI transformation engagements: discover and frame executive problems, design and build production-credible PoAs (data pipelines, integrations, LLM/RAG apps) within 2–3 weeks, present to executives, hand off to delivery, mentor junior FDEs, and convert PoAs into full engagements.

Build and operate production infrastructure for entity-focused AI agents. Responsibilities include scheduled benchmarking and golden gates, scalable entity-agent pipelines, evidence adapters, replayable vendor stores, agent-platform deployment, review and batch tooling, rollback mechanisms, and comprehensive cost, quality, latency, and health instrumentation. The role partners with AI engineering, product, research, data, and platform teams to move prototypes into reliable, self-service production systems.

Lead technical direction for company-wide internal AI initiatives. Architect, build, and operate production AI services, agents, integrations, and automations; evaluate build-versus-buy decisions; establish reusable patterns; partner across business and technical teams; guide architecture and code reviews; ensure data permissions, reliability, monitoring, recovery, documentation, and cost accountability.

Design, train, deploy, monitor, and optimize speech and NLP models for a voice-agent platform. Build MLOps pipelines, APIs, and inference services while developing Android and iOS applications that integrate AI, voice, real-time communication, and backend services. Create frontend prototypes and dashboards, conduct model experiments, evaluate vendors, optimize performance, document systems, and maintain engineering quality across collaborative teams.

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.

Leads the design, development, governance, and enterprise adoption of secure Generative AI platforms and applications. Responsibilities include architecting RAG and agent-based solutions, developing APIs and reusable frameworks, establishing evaluation and prompt engineering standards, ensuring security and responsible AI practices, mentoring engineers, influencing architecture, and driving technical strategy across teams.

Lead the architecture and delivery of enterprise generative AI solutions across document processing, language, vision, and agentic systems. Design Azure-based pipelines, RAG workflows, multi-agent solutions, integrations, and human-in-the-loop processes. Partner with stakeholders, lead engineering teams, and establish MLOps, governance, security, cost optimization, and responsible AI practices.

Partner with enterprise customers to identify AI use cases, validate feasibility, and implement tailored solutions using WRITER’s platform, APIs, and Knowledge Graph. Architect applications, templates, and integrations; explain AI capabilities to technical and executive audiences; collaborate with Product and Engineering; develop scalable integration processes and documentation; and drive customer adoption, expansion, and measurable business outcomes.

Build, maintain, and improve production AI services for document understanding including LLM pipelines, background jobs, databases, and internal tools. Implement features, debug production issues, write tests, and contribute to evaluation frameworks to improve model quality and system reliability. Collaborate with a small engineering team and take increasing ownership over platform components.

Build applied AI and automation solutions that transform operational workflows into secure, observable, production-ready services. Develop AI agents, orchestration workflows, APIs, integrations, evaluation frameworks, and automation infrastructure across Azure, .NET, C#, Python, React, and SQL. Improve AI quality through grounding, retrieval, evaluation, observability, and responsible AI practices. Partner with engineers, stakeholders, and end users to translate business processes into effective technical solutions.

Unpaid AI engineering internship to design, build, and deploy ML solutions (NLP, computer vision, recommenders). Work on data preprocessing, model training/fine-tuning, containerized FastAPI microservices, and scalable deployments on Google Cloud Run. Contribute to code reviews, GitHub repos, and integration of third-party AI APIs.

Build and operate production AI agents and agentic workflows for Workstream’s AI-Native Services initiative. Responsibilities include orchestration, integrations, permissions, evaluation harnesses, human-review tooling, monitoring, shadow-mode testing, incident response, and productizing customer-specific solutions. The role is a founding engineering position with significant ownership over architecture, reliability, reusable components, and technical standards while working directly with design customers.