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About This Role
External Description:
*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.* Living Our Values
All associates are guided by Our Values. Our Values are the unifying foundation of our companies. We strive to ensure that every decision we make and every action we take demonstrates Our Values. We believe that putting Our Values into practice creates lasting benefits for all of our associates, shareholders, and the communities in which we live.
Why Join Us
- Career Growth: Advance your career with opportunities for leadership and personal development.
- Culture of Excellence: Be part of a supportive team that values your input and encourages innovation.
- Competitive Benefits: Enjoy a comprehensive benefits package that looks after both your professional and personal needs.
Total Rewards
Our Total Rewards package underscores our commitment to recognizing your contributions. We offer a competitive and fair compensation structure that includes base pay and performance\-based rewards. Compensation is based on skill set, experience, qualifications, and job\-related requirements. Our comprehensive benefits package includes medical, dental, and vision insurance, wellness programs, retirement plans, and generous paid leave. Discover more about what we offer by visiting our Benefits page. A Day In The Life
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.
As a Sr Mgr, Full\-Stack AI Eng you will:* Team Leadership \& People Management 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.
- Hands\-On Engineering
+ 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.
- Engineering Standards \& Quality
+ 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.
- Technical Guidance \& Architecture
+ 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.
- Hiring \& Talent Development
+ 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.
- Stakeholder Partnership
+ 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.
+ Process \& Operational Excellence 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.
What We Need From You
- Bachelor's Degree Computer Science, Software Engineering, Information Systems, or a related technical field Req
- 10\+ years Software engineering experience with a strong full\-stack background Required
- 5\+ years of experience leading or managing software engineering teams Required
- Demonstrated ability to hire, develop, and retain engineering talent Required
- Experience setting and enforcing engineering standards across a team (code review, testing, documentation, security) Required
- Experience delivering AI or data\-intensive applications in a production environment Required
- Hands\-on experience engineering a development workflow using AI coding tools (Claude Code, Codex, Augment Code, or equivalent) Required
- Experience with full\-stack web development (frontend frameworks such as React or Angular; Python\-based backends such as FastAPI, Django, or Flask) Required
- Experience managing delivery across multiple concurrent projects or workstreams Required
- Experience with CI/CD, cloud\-native deployments, or infrastructure\-as\-code (AWS preferred) Required
- 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 including data scientists and product owners Preferred
- 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.
Physical and Environmental Requirements
The physical requirements described here are representative of those that must be met by an associate to successfully perform the essential functions of the job. While performing the duties of the job, the associate is required on a daily basis to analyze and interpret data, communicate, and remain in a stationary position for a significant amount of the work day and frequently access, input, and retrieve information from the computer and other office productivity devices. The associate is regularly required to move about the office and around the corporate campus. The associate must frequently move up to 10 pounds and occasionally move up to 25 pounds. Travel Requirements
20%\-30%: The position is open to in\-office and remote work in Houston or Austin. If remote (\>50 miles from office) it is expected that the associate will be in office at least 4 days a month. This may vary based upon project meetings and events. Join Us
The Friedkin Group and its affiliates are committed to ensuring equal employment opportunities, including providing reasonable accommodations to individuals with disabilities. If you have a disability and would like to request an accommodation, please contact us at TalentAcquisition@friedkin.com. We celebrate diversity and are committed to creating an inclusive environment for all associates.
We are seeking candidates legally authorized to work in the United States, without Sponsorship.
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Role Details
About This Role
This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.
The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.
Across the 3,708 AI roles we're tracking, AI Engineering Manager positions make up 0% of the market. At The Friedkin Group, this role fits into their broader AI and engineering organization.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
What the Work Looks Like
Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
Skills Required
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.
Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
Compensation Benchmarks
AI Engineering Manager roles pay a median of $249,650 based on 10 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
The Friedkin Group AI Hiring
The Friedkin Group has 2 open AI roles right now. They're hiring across AI Engineering Manager, AI/ML Engineer. Based in Houston, TX, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into AI Engineering Manager roles include Software Engineer, Data Scientist, Data Analyst.
From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.
Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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