Sr. Manager, Full-Stack AI Engineering; Remote from Houston or Austin TX

Applicants must be legally authorized to work in the United States at the time of hire and must not require employer sponsorship now or in the future. This position is not eligible for employment visa sponsorship, and the company will not assume sponsorship obligations for existing visa holders.

The Senior Manager, Full‑Stack AI Engineering leads a team of Full‑Stack AI Engineers that design and build enterprise‑grade applications that operationalize analytical models, machine learning models, Gen AI solutions, and AI‑powered web applications. The manager stays hands‑on, contributing to software engineering, system design, development and deployment playbooks, and engineering work on active projects. The role reports directly to the Director of Data Science and partners closely with product owners, data engineers, and AI scientists.

• Team Leadership & People Management:
  Directly manage a team of Full‑Stack AI Engineers across multiple project pods; conduct 1:1s, coaching, performance reviews, and build a high‑performing team culture.
• Hands‑On Engineering:
  • Actively contribute as a Full‑Stack AI Engineer on project work.
  • Design end‑to‑end architecture spanning frontend, backend, and data layers.
  • Build data pipelines, backend APIs, AI‑powered features, and front‑end applications following the team’s standards; conduct code reviews and architecture reviews.
• Engineering Standards & Quality:
  • Define and enforce engineering standards (code quality, testing, security, documentation).
  • Conduct or oversee solution architecture and documentation reviews.
  • Keep abreast of AI tooling advancements and share best practices; ensure AI‑generated code meets the same quality bar as hand‑authored code.
  • Champion responsible use of AI coding tools and establish team‑wide norms.
• Technical Guidance & Architecture:
  • Provide hands‑on technical guidance on solution design, full‑stack architecture, AI and LLM integration patterns, and Databricks platform usage.
  • Participate in architecture reviews and key technical decisions.
• Hiring & Talent Development:
  • Partner with the Director of Data Science and HR to define hiring needs, assess candidates, and set learning paths.
  • Develop engineers through structured feedback, stretch assignments, and learning opportunities.
  • Build a diverse team with complementary strengths across cloud engineering.
• Stakeholder Partnership:
  • Work closely with product owners, data engineers, AI scientists, and the Director of Data Science to align on engineering priorities and delivery.
  • Represent the engineering team in cross‑functional planning and communicate capacity and constraints clearly.
  • Establish and continuously improve engineering processes: sprint cadences, code review workflows, support processes, and knowledge sharing.

• Bachelor’s Degree in Computer Science, Software Engineering, Information Systems, or a related technical field.
• 10+ years of software engineering experience with a strong full‑stack background.
• 5+ years of experience leading or managing software engineering teams.
• Demonstrated ability to hire, develop, and retain engineering talent.
• Experience setting and enforcing engineering standards across a team.
• Experience delivering AI or data‑intensive applications in production.
• Hands‑on experience engineering a development workflow using AI coding tools (e.g., Claude Code, Codex, Augment Code).
• Experience with full‑stack web development (frontend frameworks such as React or Angular; Python‑based backends such as FastAPI, Django, or Flask).
• Experience managing delivery across multiple concurrent projects or work streams.
• Experience with CI/CD, cloud‑native deployments, or infrastructure‑as‑code (AWS preferred).
• Preferred: experience integrating LLMs, RAG pipelines, or agentic AI frameworks into production applications.
• Preferred: experience with Databricks or Lakehouse architectures.
• Preferred: experience working in agile/scrum delivery models with cross‑functional teams.
• Technical credibility to assess code quality, architectural decisions, and engineering complexity.
• People leadership: coaching, development, and accountability.
• Delivery ownership: accountability for team output and…