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

The Friedkin Group•Houston, TX

1d•Remote

About The Position

The Sr. Manager, Full-Stack AI Engineering, a technical engineering manager, leads a team of Full-Stack AI Engineers that design and build enterprise-grade applications that operationalize analytical models/engines, machine learning models/systems, Gen AI solutions from data integration pipelines, backend APIs, optimization engine, AI Agents to front-end and AI-powered web application. This role stays hands-on, directly contributing to software engineering technology stack, system design pattern, development & deployment playbooks, and engineering work on active projects. The Sr. Manager reports directly to the Director, Data Science, and partners closely with product owners, data engineers, and AI scientists on various AI/analytical oriented projects. People management responsibilities include hiring, coaching, performance management, and setting engineering standards across the team.

Requirements

Bachelor's Degree Computer Science, Software Engineering, Information Systems, or a related technical field
10+ years 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 (code review, testing, documentation, security)
Experience delivering AI or data-intensive applications in a production environment
Hands-on experience engineering a development workflow using AI coding tools (Claude Code, Codex, Augment Code, or equivalent)
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 workstreams
Experience with CI/CD, cloud-native deployments, or infrastructure-as-code (AWS preferred)
Experience integrating LLMs, RAG pipelines, or agentic AI frameworks into production applications
Technical credibility: depth sufficient to assess code quality, architectural decisions, and engineering complexity without being a bottleneck.
People leadership: ability to coach, develop, and hold engineers accountable in a way that builds trust and improves performance over time.
Delivery ownership: takes accountability for team output, proactively manages risks, and communicates status clearly to stakeholders.
AI tooling judgment understands both the capabilities and limitations of AI coding tools and can set meaningful standards for their use.
Strong cross-functional collaboration: comfortable working alongside product owners, data engineers, AI scientists, and business stakeholders.
Ability to operate in a fast-moving environment where priorities shift and the tooling landscape evolves rapidly.
Clear and direct communicator: able to translate technical constraints into language that non-technical stakeholders can act on.
Process pragmatism improves team processes where it matters, avoids overhead where it doesn't.

Nice To Haves

Experience with Databricks or Lakehouse architectures
Experience working in agile/scrum delivery models with cross-functional teams including data scientists and product owners

Responsibilities

Directly manage a team of Full-Stack AI Engineers across multiple concurrent project pods.
Conduct regular 1:1s, provide ongoing coaching and feedback, set clear performance expectations, and lead formal performance reviews.
Build a high-performing, accountable team culture that takes pride in quality and delivery.
Actively contribute as a Full-Stack AI Engineer on project work alongside the team.
Design end-to-end architecture spanning frontend, backend, and data layers.
Design and build data pipelines, backend APIs, AI-powered features, and front-end applications using the same stack and standards expected of the team.
Maintain technical depth across the stack to provide credible guidance, conduct meaningful code reviews, architecture review and set a high bar for engineering quality through example.
Define and enforce engineering standards across the team — covering code quality, testing practices, security, documentation.
Conduct or oversee solution architecture, code reviews and documentation reviews on high-impact work.
Stay current on AI tooling advancements and share best practices across the team.
Ensure that AI-generated code meets the same quality bar as hand-authored code.
Champion the effective and responsible use of AI coding tools across the team.
Develop and formulate spec-driven AI coding practice with responsible use of AI coding tools (Claude Code, Codex, Augment Code, etc.).
Evaluate new tools and practices, establish team-wide norms for AI-assisted development, and ensure engineers are using these tools in ways that increase quality and delivery confidence — not just speed.
Provide hands-on technical guidance to engineers on solution design, full-stack architecture, AI and LLM integration patterns, and Databricks platform usage.
Participate in architecture reviews and contribute to key technical decisions.
Remain close enough to the work to assess complexity, quality, and risk.
Partner with the Director of Data Science and HR to define hiring needs, assess candidates, and define learning path of each team member to grow the team.
Develop engineers through structured feedback, stretch assignments, and learning opportunities.
Build a team with complementary strengths across cloud engineering, including frontend, backend, data integration, DevOps/InfraOps, ML system integration and AI tooling.
Work closely with product owners, data engineers, AI scientists, and the Director of Data Science to align on engineering priorities and delivery.
Communicate team capacity and technical constraints clearly.
Represent the engineering team’s perspective in cross-functional planning discussions.
Establish and continuously improve the team's engineering processes: sprint cadences, code review workflows, support processes, and knowledge sharing.
Identify systemic inefficiencies and drive improvements that make the team more effective over time.