Top AI Engineer Jobs in Austin, TX

The Senior ML/AI Engineer will enhance developer productivity for AV engineers at GM by designing, implementing scalable, reliable systems and mentoring team members.

Take Deepgram's speech models and optimize, compile, and run them on resource-constrained embedded and edge hardware. Work across model compression, runtime performance (C/C++/Rust), RTOS/bare-metal and embedded Linux environments, vendor NPUs/DSPs and inference runtimes, benchmarking, deployment pipelines, OTA updates, and partner with silicon vendors to enable real-time, low-power on-device voice experiences.

Design, build, and operate large-scale LLM infrastructure and data platforms for training, fine-tuning, and inference. Provision GPU clusters, optimize GPU utilization, implement model lifecycle management, deploy inference frameworks, create evaluation and observability systems, and collaborate with data scientists to productionize AI capabilities. Mentor engineers and enforce MLOps/DataOps best practices.

Design, build, deploy, and iterate production-ready AI agents and LLM-based solutions that integrate with enterprise workflows. Rapidly prototype, evaluate, and optimize agents for performance, cost, and adoption. Partner across functions to redesign workflows, contribute to an AI Center of Excellence, and ensure responsible, secure, and scalable agent development.

Build and scale AI platform infrastructure for internal agents across AWS/GCP. Integrate identity (Okta, OAuth2/OIDC), implement agentic security, containerized deployments (Docker/Kubernetes), CI/CD and Terraform-based pipelines, run vendor evaluations, and design observability/telemetry for LLM-driven workflows while collaborating with security, identity, and engineering teams.

Lead design, build, and operate scalable observability systems for distributed cloud and on-vehicle environments. Contribute code and reviews, create technical designs, build tooling to collect observability signals, run PoCs for new tools, partner with engineering teams, define SLIs/SLOs, guide roadmap decisions, and mentor engineers to improve developer productivity and reliability.

Build the core AI platform and high-performance Rust backend powering an AI-native trading copilot. Implement streaming responses, low-latency tool execution, caching, orchestration of models and tools, secure APIs, auditing/tracing, and safe, auditable trading action infrastructure. Partner with product and frontend to enable AI-native UX and translate trading domain requirements into resilient platform primitives.

Lead ownership of reliability, evaluation, and safety for an AI copilot in a trading platform. Build eval systems, benchmarks, automated quality gates, monitoring, and model improvement loops; partner with engineering and product to define safe tool contracts and ensure predictable, auditable assistant behavior in trading workflows.

As a Principal Data Engineer, you will design and implement LLM, AI-powered security data platforms, mentor engineers, and drive the adoption of data solutions across teams.

Lead development and operation of CI platform components (RBE, FUSE) to enable scalable build, test, and developer workflows. Collaborate across teams, own projects end-to-end, drive engineering best practices, mentor engineers, perform design/code reviews, and support integration of internal tools with the FUSE-based file system to improve developer productivity for AV and ML workloads.

Lead design and implementation of high-scale, resilient AI inference data plane services. Architect distributed LLM serving systems, optimize performance (tensor/data parallelism, KV-cache routing), build on Kubernetes-native inference frameworks, contribute to open-source projects, mentor engineers, and operate production services with strong observability and SLOs.

Senior SRE on the IT Operations team owning reliability, monitoring, and incident response for AI infrastructure. Build automation, CI/CD and Kubernetes tooling, improve observability and documentation, and develop internal full-stack tools using Go or Python. Partner with Infrastructure, Security, and Compliance to scale secure, resilient AI deployment pipelines.

Design, build, and own scalable data pipelines and evaluation systems that power production AI features and internal analytics. Ensure data quality across ingestion, modeling, and reporting, collaborate with ML and analytics teams, deploy and monitor ML systems, and establish standards for data work and evaluation.

Embed with business teams to map workflows and build AI-driven, agentic automations. Design and maintain Workato-based integrations, develop scalable microservices, own solution lifecycle, and ensure observability and security for enterprise SaaS data flows.

Lead design and scaling of production AI agent systems for Bee, architect Python services and agent workflows (PydanticAI, GCP), define prompt/response schemas and fallbacks, build evaluation/experimentation and observability, and partner across Product, Design, and Data to deliver responsible AI-driven product features.

Partner with attorneys to design, build, deploy, and maintain AI-enabled legal workflows (prompting, RAG, templates, evaluations). Provide training, change management, governance-aligned guardrails, monitor adoption and performance, and iterate on workflows with cross-functional teams.

Design and build developer-facing AI platform tools, define end-to-end AI development lifecycles and guardrails, deliver reusable tooling and integration patterns, drive cross-functional adoption, mentor engineers, and enable secure, scalable LLM-powered applications.

Build, deploy, and maintain AI-powered agents, APIs, and applications for People operations on Snowflake. Turn messy business requirements into production systems, integrate with Workday/Notion/case tools, ensure data governance and security, design multi-model LLM reliability controls, and operate full lifecycle including CI/CD, monitoring, and incident fixes while partnering with non-technical stakeholders.

Design and implement an MLIR-based AI compiler for GPU inference: develop graph representations and dialects, optimize performance and memory, define APIs, generate kernels, and collaborate with framework and hardware teams to enable new model patterns and GPU features.

Design and optimize AI communication middleware for distributed multi-node, multi-GPU training. Implement C/C++ performance-critical features, improve GPU communication paths, profile and tune frameworks (PyTorch, TensorFlow, JAX, DeepSpeed), develop benchmarks, contribute upstream, and collaborate with kernel, driver, switch, and systems teams to validate and deploy high-performance networking for AI/HPC.

Design, build, and deploy LLM-powered production systems (agents, RAG pipelines, voice agents). Integrate with enterprise stacks (CRMs, ERPs, data warehouses), harden with evals, observability, and guardrails, run client scoping and delivery, and feed learnings into internal tooling and playbooks.

Lead AI transformation and technical pre-sales for Finance & Supply Chain ERP, driving POCs, workshops, demos, and technical win strategies. Engage C-suite and technical decision makers, collaborate with partners and engineering, address Copilot/AI Agents compliance and security concerns, and support pipeline creation, deal velocity, and competitive wins for D365 F&SC and Microsoft AI solutions.

Evaluate AI-generated coding interactions end-to-end, judging usefulness, high-level correctness, engineering judgment, explanations, and reasoning. Provide clear, opinionated feedback, distinguish quality levels, and help define standards for excellent AI-assisted developer interactions.

Contract senior engineer evaluates AI coding agent interactions (Codex, Claude Code) for engineering judgment, reasoning quality, usefulness, and alignment with strong developer practices; provides opinionated feedback and helps define high-quality interaction standards. ~10-20 hrs/week, $100-$200/hr, short-term through early May.

Lead and contribute to applied AI/ML projects across the SDLC: mentor teams, design and implement LLM-based solutions (RAG, copilots), architect integrations, manage data persistence choices, CI/CD, and present solutions to clients while influencing technical direction.