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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
As a Full\-Stack AI Engineer you will design and build enterprise\-grade applications that operationalize analytical models, machine learning models, Gen AI solutions from ingestion pipelines and backend APIs to front\-end applications and AI\-powered features. You are the primary builder on cross\-functional project pods that include product owners, enterprise data engineers, and data scientists. This role sits at the intersection of data engineering, software engineering, AI engineering, and AI scientist, and is the foundational capability that enables the team's operating model, requiring strong full\-stack development skills and the ability to integrate with data pipelines, ML models (via API), and data serving on Databricks. You leverage AI coding tools (Claude Code, Codex, Augment Code, etc.) to accelerate delivery and maintain a high pace of iteration without sacrificing quality.
As a Full Stack AI Engineer you will:* System Design \& Architecture
- Design end\-to\-end solutions spanning frontend, backend, and data layers.
- Define patterns for scalable AI\-enabled applications.
- Contribute to architecture decisions and participate in technical reviews across the data and AI ecosystem.
- End\-to\-End Application
- Translate analytical outputs and model results into user experiences that business stakeholders can act on directly. Apply strong product thinking to front\-end design and usability.
- Design and build user\-facing applications (web apps, APIs, workflows) that enable interaction with data science and AI models.
- Build intuitive dashboards, data applications, and self\-serve analytics tools using modern front\-end frameworks.
- Develop full\-stack solutions using technologies such as React, Angular, and Python\-based backends (Django, FastAPI, Flask etc.)
- Ensure solutions are scalable, secure, and enterprise ready.
- Data Pipeline \& Integration
- Build and maintain data ingestion pipelines, ETL workflows, and integrations with the Databricks Lakehouse platform.
- Build RAG pipelines, data pipeline to sync Agent memory systems
- Connect applications to data sources, feature stores, workflows and ML model endpoints.
- Ensure data quality, reliability, and performance across the pipeline.
- AI \& LLM Feature Development Integrate agentic workflow, RAG pipelines, and AI agent into production applications.
- Build and deploy AI\-powered features including semantic search, document understanding, conversational interfaces, and automated workflows.
- Evaluate and select appropriate AI tools and APIs for each use case.
- AI\-Assisted Engineering
- Actively leverage AI coding tools (Claude Code, Codex, Augment Code) as a core part of the development workflow.
- Stay current on AI tooling advancements and share best practices across the team.
- Maintain high code quality standards when using AI\-generated code.
- DevOps \& Engineering Quality
- Implement CI/CD pipelines, automated testing, and observability for all production systems.
- Contribute to formulate software engineering best practices including code review, documentation, testing, and security standards.
- Ensure observability, monitoring, and reliability of applications.
What We Need From You
- Bachelor's Degree Computer Science, Software Engineering, Information Systems, or a related technical field
- 8\+ years of experience building production\-grade software applications Required
- Strong full\-stack development experience (frontend \+ backend) Required
- Experience building RESTful APIs and backend services Required
- Experience with data\-intensive applications or data platform integrations Required
- Experience actively using AI coding tools (Claude Code, Codex, Augment Code) as part of day\-to\-day development Required
- Experience with front\-end frameworks such as React, Angular, or Next Required
- Experience working in agile/scrum delivery models with cross\-functional teams Preferred
- Experience integrating LLMs, RAG pipelines, or AI APIs into production applications Preferred
- Experience with Databricks, or Lakehouse architectures Preferred
- Experience working with cloud\-native web application development and deployment (AWS, Azure) Preferred
- Experience with CI/CD, infrastructure\-as\-code, or cloud\-native deployments (AWS preferred) Preferred
- End\-to\-end ownership mindset takes accountability for the full lifecycle of a product from pipeline to UI, not just assigned components.
- AI\-native engineering fluency: actively and effectively uses AI coding tools as a multiplier, not just a convenience.
- Product thinking: focuses on building usable, valuable solutions that solve real business problems.
- Strong collaboration and communication skills across product, data science, and business stakeholder teams.
- Ability to operate across multiple layers of the stack without deep specialization in any single area.
- Ability to work effectively in ambiguous, fast\-moving environments with evolving requirements.
- Intellectually curious with a strong drive to stay current on AI tooling, frameworks, and best practices.
- Strong engineering discipline: code quality, testing, documentation, and security awareness even at high delivery velocity.
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.
\#LI\-TW1
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At The Friedkin Group, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
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/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
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).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
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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