Top Tech Jobs & Startup Jobs in Austin, TX

Lead technical enterprise AI sales by conducting discovery, delivering tailored demos, running architecture and security workshops, responding to technical proposals, and designing proofs of value. Translate AI capabilities into measurable business outcomes, scope deployments and integrations, partner with Account Executives and engineering teams, support customer production adoption, identify expansion opportunities, mentor Solutions Architects, and provide product feedback. The role requires hands-on knowledge of generative AI, cloud infrastructure, data preparation, security, compliance, and model deployment.

Build and optimize scalable frontend applications for Seekr’s AI platform using React and Next.js. Collaborate with ML, backend, DevOps, QA, and Product Design teams to rapidly deliver performant web features. Contribute to technical direction, automated testing, API integration, deployments, and continuous improvement of frontend architecture and user experiences.

Forward Deployed Engineers embed with client teams to operationalize AI prototypes into production systems. They design data pipelines and retrieval frameworks, fine-tune and deploy LLMs and agentic workflows, integrate AI agents across cloud and on-premises environments, and implement monitoring, guardrails, security, and performance optimization. The role also involves client collaboration, troubleshooting model behavior, and creating reusable deployment practices. Travel may range from 25% to 75%.

Lead end-to-end commercial and technical engagements to translate enterprise business problems into production-grade, explainable AI solutions. Define requirements, scope deployments, partner with engineers and designers, quantify business impact and ROI, drive adoption and training, and act as trusted advisor during implementation and change management.

Embed with client teams to productionize AI: design data pipelines and retrieval frameworks, fine-tune and deploy LLMs and agentic workflows, containerize and orchestrate across cloud and on-prem, optimize inference and monitoring, and codify deployment best practices to scale trustworthy, explainable AI solutions.

Lead and hands-on manage Seekr's AI infrastructure: design, build, and scale Kubernetes-based systems across cloud, on-prem, and edge; mentor engineers; automate operations; handle incidents and security; collaborate with software teams to ensure reliability, performance, and observability.

Design and build explainable and contestable AI capabilities for models and agents. Develop hallucination detection, continual-learning systems, and production AI features from research prototypes. Contribute across model development, inference, deployment, monitoring, evaluation, and performance optimization. Partner with product, design, and customer teams to deliver trustworthy AI workflows. The role requires strong Python and software engineering skills, modern AI systems experience, and the ability to bridge research and production engineering.

Design and productionize explainable and contestable AI capabilities for LLM, VLM, agent, and RAG systems. Develop hallucination mitigation, continual-learning, evaluation, inference, deployment, monitoring, and retrieval solutions. Translate research into production features, optimize model serving, and collaborate with product, design, and customer teams. The role requires strong Python and software engineering skills, research-to-engineering fluency, and experience evaluating complex AI tradeoffs.

Lead the reliability, stability, observability, and scalability of Seekr’s software platform. Design and implement systems, networks, and services that meet SLAs; develop monitoring, automation, and failure-detection solutions; conduct load testing; manage observability tools; troubleshoot production and deployment issues; and support incident response. Collaborate closely with software engineering teams across hybrid cloud and on-premises environments.

Build and scale applications and tools powering AI products, agents, and enterprise solutions. Responsibilities include shipping production-ready features, shaping scalable and reliable architecture, mentoring engineers, driving technical decisions and engineering best practices, collaborating with Product, QA, and business teams, and translating abstract ideas into actionable engineering work. The role requires expertise in Python, distributed systems, APIs, databases, containers, streaming technologies, testing, CI/CD, and AI-assisted development.