Senior Full-Stack AI Engineer

Role overview

Key Responsibilities

• check_circle Full-Stack Architecture & Execution – Architect, develop, and own end-to-end delivery of robust cloud services (Python, Node/TypeScript, or Go) and responsive front-end interfaces (React, Next.js, Vite, Chakra UI).
• check_circle AI & LLM Integration – Design and deploy intelligent features, multi-step agentic workflows, and RAG systems using platforms like OpenAI, Anthropic (Claude), LangChain/LangGraph, or custom vector databases.
• check_circle Spec-Driven Pod Collaboration – Partner closely in small 'triad' pods (Product, Design, Engineering) to translate specs into high-quality code with fast feedback loops and quick customer-facing release cadences.
• check_circle Modern Workflows & Production Excellence – Leverage modern AI-assisted development tools (e.g., Cursor) to boost velocity and code quality. Build and maintain CI/CD pipelines, containerized environments (Kubernetes, Docker), and IaC (Terraform).
• check_circle Technical Mentorship & Standards – Serve as a technical anchor on client projects. Mentor peer engineers through constructive code reviews, establishing clean API patterns, and championing best practices without the burden of administrative direct-report management.

Key Qualifications

• Experience: 6+ years of total software engineering experience, with a track record of building and shipping production-grade applications.
• Backend Mastery: Strong expertise in Python (FastAPI, Flask, Django) or similar modern backend languages, including microservices design, RESTful APIs, and asynchronous data pipelines.
• Modern Frontend: Proficient with contemporary frontend stacks (React, Next.js, Vite, TypeScript, Storybook, Chakra UI, or similar component libraries).
• AI/ML & LLM Exposure: Hands-on experience integrating LLMs, prompt engineering, vector stores, or agentic frameworks into production environments.
• Cloud & DevOps: Familiarity with AWS or Azure cloud infrastructure, containerization (Kubernetes, Docker), IaC (Terraform), and modern CI/CD practices.
• Product-Led Mindset: Experience in startup-like, high-ambition contexts where speed, adaptability, pragmatism, and strong engineering judgment are essential.

Preferred / Nice-to-Have Skills

• Direct experience with model context protocols (MCP), LlamaIndex, CrewAI, or enterprise data lakehouses (e.g., Databricks).
• Deep understanding of AI observability, evaluation frameworks, security, and data privacy protocols.
• Experience utilizing Cursor or similar AI-native tools to optimize development speed.
• Proximity to Austin, TX for occasional whiteboarding and collaborative pod sessions.