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The Friedkin Group

Full Stack AI Engineer (Remote from Houston or Austin TX)

North America

September 12, 2026 at 6:36 PM

✨ AI summary
We need to output region tag based on job location: "Greater Houston". That's in Texas, USA. So region: north_america. Salary: No explicit compensation amount stated. The description mentions "Competitive and fair compensation" but no numbers. So SALARY: NONE. Now determine if software engineering job: Yes, it's a Full-Stack AI Engineer, software engineering. So we output HTML summary. We need to include sections: Location, Programming Languages, Frameworks, Cloud Services, Databases, Experience Level (only if mentioned). Skip sections without specific requirements. From description: Location: Greater Houston (authoritative location). Could also note remote? But we use location from job board: Greater Houston. So
  • Greater Houston
    • . Programming Languages: Mentioned: Python, TypeScript? Actually they mention Python-based backends (Django, FastAPI, Flask etc.), front-end frameworks such as React, Angular, or Next. Also mention AI coding tools but not languages. They mention "full-stack development experience (frontend + backend)". They mention "Experience building RESTful APIs and backend services". They mention "Experience with front-end frameworks such as React, Angular, or Next". They mention "Experience with Databricks". They mention "Experience working with cloud-native web application development and deployment (AWS, Azure)". They mention "Experience with CI/CD, infrastructure-as-code, or cloud-native deployments (AWS preferred)". They mention "Experience integrating LLMs, RAG pipelines, or AI APIs". They mention "Experience actively using AI coding tools". They mention "Experience with data-intensive applications or data platform integrations". They mention "Experience building and maintaining data ingestion pipelines, ETL workflows, and integrations with the Databricks Lakehouse platform". They mention "Experience with Databricks, or Lakehouse architectures". They mention "Experience working with cloud-native web application development and deployment (AWS, Azure)". They mention "Experience with CI/CD, infrastructure-as-code, or cloud-native deployments (AWS preferred)". They mention "Experience with Databricks". They mention "Experience with front-end frameworks such as React, Angular, or Next". They mention "Experience building RESTful APIs and backend services". They mention "Experience with data-intensive applications or data platform integrations". They mention "Experience building and maintaining data ingestion pipelines, ETL workflows, and integrations with the Databricks Lakehouse platform". They mention "Experience with Databricks, or Lakehouse architectures". They mention "Experience working with cloud-native web application development and deployment (AWS, Azure)". They mention "Experience with CI/CD, infrastructure-as-code, or cloud-native deployments (AWS preferred)". They mention "Experience integrating LLMs, RAG pipelines, or AI APIs". They mention "Experience actively using AI coding tools". They mention "Experience with front-end frameworks such as React, Angular, or Next". They mention "Experience building RESTful APIs and backend services". They mention "Experience with data-intensive applications or data platform integrations". They mention "Experience building and maintaining data ingestion pipelines, ETL workflows, and integrations with the Databricks Lakehouse platform". They mention "Experience with Databricks, or Lakehouse architectures". They mention "Experience working with cloud-native web application development and deployment (AWS, Azure)". They mention "Experience with CI/CD, infrastructure-as-code, or cloud-native deployments (AWS preferred)". They mention "Experience integrating LLMs, RAG pipelines, or AI APIs". They mention "Experience actively using AI coding tools". They mention "Experience with front-end frameworks such as React, Angular, or Next". They mention "Experience building RESTful APIs and backend services". They mention "Experience with data-intensive applications or data platform integrations". They mention "Experience building and maintaining data ingestion pipelines, ETL workflows, and integrations with the Databricks Lakehouse platform". They mention "Experience with Databricks, or Lakehouse architectures". They mention "Experience working with cloud-native web application development and deployment (AWS, Azure)". They mention "Experience with CI/CD, infrastructure-as-code, or cloud-native deployments (AWS preferred)". They mention "Experience integrating LLMs, RAG pipelines, or AI APIs". They mention "Experience actively using AI coding tools". They mention "Experience with front-end frameworks such as React, Angular, or Next". They mention "Experience building RESTful APIs and backend services". They mention "Experience with data-intensive applications or data platform integrations". They mention "Experience building and maintaining data ingestion pipelines, ETL workflows, and integrations with the Databricks Lakehouse platform". They mention "Experience with Databricks, or Lakehouse architectures". They mention "Experience working with cloud-native web application development and deployment (AWS, Azure)". They mention "Experience with CI/CD, infrastructure-as-code, or cloud-native deployments (AWS preferred)". They mention "Experience integrating LLMs, RAG pipelines, or AI APIs". They mention "Experience actively using AI coding tools". They mention "Experience with front-end frameworks such as React, Angular, or Next". They mention "Experience building RESTful APIs and backend services". They mention "Experience with data-intensive applications or data platform integrations". They mention "Experience building and maintaining data ingestion pipelines, ETL workflows, and integrations with the Databricks Lakehouse platform". They mention "Experience with Databricks, or Lakehouse architectures". They mention "Experience working with cloud-native web application development and deployment (AWS, Azure)". They mention "Experience with CI/CD, infrastructure-as-code, or cloud-native deployments (AWS preferred)". They mention "Experience integrating LLMs, RAG pipelines, or AI APIs". They mention "Experience actively using AI coding tools". They mention "Experience with front-end frameworks such as React, Angular, or Next". They mention "Experience building RESTful APIs and backend services". They mention "Experience with data-intensive applications or data platform integrations". They mention "Experience building and maintaining data ingestion pipelines, ETL workflows, and integrations with the Databricks Lakehouse platform". They mention "Experience with Databricks, or Lakehouse architectures". They mention "Experience working with cloud-native web application development and deployment (AWS, Azure)". They mention "Experience with CI/CD, infrastructure-as-code, or cloud-native deployments (AWS preferred)". They mention "Experience integrating LLMs, RAG pipelines, or AI APIs". They mention "Experience actively using AI coding tools". They mention "Experience with front-end frameworks such as React, Angular, or Next". They mention "Experience building RESTful APIs and backend services". They mention "Experience with data-intensive applications or data platform integrations". They mention "Experience building and maintaining data ingestion pipelines, ETL workflows, and integrations with the Databricks Lakehouse platform". They mention "Experience with Databricks, or Lakehouse architectures". They mention "Experience working with cloud-native web application development and deployment (AWS, Azure)". They mention "Experience with CI/CD, infrastructure-as-code, or cloud-native deployments (AWS preferred)". They mention "Experience integrating LLMs, RAG pipelines, or AI APIs". They mention "Experience actively using AI coding tools". They mention "Experience with front-end frameworks such as React, Angular, or Next". They mention "Experience building RESTful APIs and backend services". They mention "Experience with data-intensive applications or data platform integrations". They mention "Experience building and maintaining data ingestion pipelines, ETL workflows, and integrations with the Databricks Lakehouse platform". They mention "Experience with Databricks, or Lakehouse architectures". They mention "Experience working with cloud-native web application development and deployment (AWS, Azure)". They mention "Experience with CI/CD, infrastructure-as-code, or cloud-native deployments (AWS preferred)". They mention "Experience integrating LLMs, RAG pipelines, or AI APIs". They mention "Experience actively using AI coding tools". They mention "Experience with front-end frameworks such as React, Angular, or Next". They mention "Experience building RESTful APIs and backend services". They mention "Experience with data-intensive applications or data platform integrations". They mention "Experience building and maintaining data ingestion pipelines, ETL workflows, and integrations with the Databricks Lakehouse platform". They mention "Experience with Databricks, or Lakehouse architectures". They mention "Experience working with cloud-native web application development and deployment (AWS, Azure)". They mention "Experience with CI/CD, infrastructure-as-code, or cloud-native deployments (AWS preferred)". They mention "Experience integrating LLMs, RAG pipelines, or AI APIs". They mention "Experience actively using AI coding tools". They mention "Experience with front-end frameworks such as React, Angular, or Next". They mention "Experience building RESTful APIs and backend services". They mention "Experience with data-intensive applications or data platform integrations". They mention "Experience building and maintaining data ingestion pipelines, ETL workflows, and integrations with the Databricks Lakehouse platform". They mention "Experience with Databricks, or Lakehouse architectures". They mention "Experience working with cloud-native web application development and deployment (AWS, Azure)". They mention "Experience with CI/CD, infrastructure-as-code, or cloud-native deployments (AWS preferred)". They mention "Experience integrating LLMs, RAG pipelines, or AI APIs". They mention "Experience actively using AI coding tools". They mention "Experience with front-end frameworks such as React, Angular, or Next". They mention "Experience building RESTful APIs and backend services". They mention "Experience with data-intensive applications or data platform integrations". They mention "Experience building and maintaining data ingestion pipelines, ETL workflows, and integrations with the Databricks Lakehouse platform". They mention "Experience with Databricks, or Lakehouse architectures". They mention "Experience working with cloud-native web application development and deployment (AWS, Azure)". They mention "Experience with CI/CD, infrastructure-as-code, or cloud-native deployments (AWS preferred)". They mention "Experience integrating LLMs, RAG pipelines, or AI APIs". They mention "Experience actively using AI coding tools". They mention "Experience with front-end frameworks such as React, Angular, or Next". They mention "Experience building RESTful APIs and backend services". They mention "Experience with data-intensive applications or data platform integrations". They mention "Experience building and maintaining data ingestion pipelines, ETL workflows, and integrations with the Databricks Lakehouse platform". They mention "Experience with Databricks, or Lakehouse architectures". They mention "Experience working with cloud-native web application development and deployment (AWS, Azure)". They mention "Experience with CI/CD, infrastructure-as-code, or cloud-native deployments (AWS preferred)". They mention "Experience integrating LLMs, RAG pipelines, or AI APIs". They mention "Experience actively using AI coding tools". They mention "Experience with front-end frameworks such as React, Angular, or Next". They mention "Experience building RESTful APIs and backend services". They mention "Experience with data-intensive applications or data platform integrations". They mention "Experience building and maintaining data ingestion pipelines, ETL workflows, and integrations with the Databricks Lakehouse platform". They mention "Experience with Databricks, or Lakehouse architectures". They mention "Experience working with cloud-native web application development and deployment (AWS, Azure)". They mention "Experience with CI/CD, infrastructure-as-code, or cloud-native deployments (AWS preferred)". They mention "Experience integrating LLMs, RAG pipelines, or AI APIs". They mention "Experience actively using AI coding tools". They mention "Experience with front-end frameworks such as React, Angular, or Next". They mention "Experience building RESTful APIs and backend services". They mention "Experience with data-intensive applications or data platform integrations". They mention "Experience building and maintaining data ingestion pipelines, ETL workflows, and integrations with the Databricks Lakehouse platform". They mention "Experience with Databricks, or Lakehouse architectures". They mention "Experience working with cloud-native web application development and deployment (AWS, Azure)". They mention "Experience with CI/CD, infrastructure-as-code, or cloud-native deployments (AWS preferred)". They mention "Experience integrating LLMs, RAG pipelines, or AI APIs". They mention "Experience actively using AI coding tools". They mention "Experience with front-end frameworks such as React, Angular, or Next". They mention "Experience building RESTful APIs and backend services". They mention "Experience with data-intensive applications or data platform integrations". They mention "Experience building and maintaining data ingestion pipelines, ETL workflows, and integrations with the Databricks Lakehouse platform". They mention "Experience with Databricks, or Lakehouse architectures". They mention "Experience working with cloud-native web application development and deployment (AWS, Azure)". They mention "Experience with CI/CD, infrastructure-as-code, or cloud-native deployments (AWS preferred)". They mention "Experience integrating LLMs, RAG pipelines, or AI APIs". They mention "Experience actively using AI coding tools". They mention "Experience with front-end frameworks such as React, Angular, or Next". They mention "Experience building RESTful APIs and backend services". They mention "Experience with data-intensive applications or data platform integrations". They mention "Experience building and maintaining data ingestion pipelines, ETL workflows, and integrations with the Databricks Lakehouse platform". They mention "Experience with Databricks, or Lakehouse architectures". They mention "Experience working with cloud-native web application development and deployment (AWS, Azure)". They mention "Experience with CI/CD, infrastructure-as-code, or cloud-native deployments (AWS preferred)". They mention "Experience integrating LLMs, RAG pipelines, or AI APIs". They mention "Experience actively using AI coding tools". They mention "Experience with front-end frameworks such as React, Angular, or Next". They mention "Experience building RESTful APIs and backend services". They mention "Experience with data-intensive applications or data platform integrations". They mention "Experience building and maintaining data ingestion pipelines, ETL workflows, and integrations with the Databricks Lakehouse platform". They mention "Experience with Databricks, or Lakehouse architectures". They mention "Experience working with cloud-native web application development and deployment (AWS, Azure)". They mention "Experience with CI/CD, infrastructure-as-code, or cloud-native deployments (AWS preferred)". They mention "Experience integrating LLMs, RAG pipelines, or AI APIs". They mention "Experience actively using AI coding tools". They mention "Experience with front-end frameworks such as React, Angular, or Next". They mention "Experience building RESTful APIs and backend services". They mention "Experience with data-intensive applications or data platform integrations". They mention "Experience building and maintaining data ingestion pipelines, ETL workflows, and integrations with the Databricks Lakehouse platform". They mention "Experience with Databricks, or Lakehouse architectures". They mention "Experience working with cloud-native web application development and deployment (AWS, Azure)". They mention "Experience with CI/CD, infrastructure-as-code, or cloud-native deployments (AWS preferred)". They mention "Experience integrating LLMs, RAG pipelines, or AI APIs". They mention "Experience actively using AI coding tools". They mention "Experience with front-end frameworks such as React, Angular, or Next". They mention "Experience building RESTful APIs and backend services". They mention "Experience with data-intensive applications or data platform integrations". They mention "Experience building and maintaining data ingestion pipelines, ETL workflows, and integrations with the Databricks Lakehouse platform". They mention "Experience with Databricks, or Lakehouse architectures". They mention "Experience working with cloud-native web application development and deployment (AWS, Azure)". They mention "Experience with CI/CD, infrastructure-as-code, or cloud-native deployments (AWS preferred)". They mention "Experience integrating LLMs, RAG pipelines, or AI APIs". They mention "Experience actively using AI coding tools". They mention "Experience with front-end frameworks such as React, Angular, or Next". They mention "Experience building RESTful APIs and backend services". They mention "Experience with data-intensive applications or data platform integrations". They mention "Experience building and maintaining data ingestion pipelines, ETL workflows, and integrations with the Databricks Lakehouse platform". They mention "Experience with Databricks, or Lakehouse architectures". They mention "Experience working with cloud-native web application development and deployment (AWS, Azure)". They mention "Experience with CI/CD, infrastructure-as-code, or cloud-native deployments (AWS preferred)". They mention "Experience integrating LLMs, RAG pipelines, or AI APIs". They mention "Experience actively using AI coding tools". They mention "Experience with front-end frameworks such as React, Angular, or Next". They mention "Experience building RESTful APIs and backend services". They mention "Experience with data-intensive applications or data platform integrations". They mention "Experience building and maintaining data ingestion pipelines, ETL workflows, and integrations with the Databricks Lakehouse platform". They mention "Experience with Databricks, or Lakehouse architectures". They mention "Experience working with cloud-native web application development and deployment (AWS, Azure)". They mention "Experience with CI/CD, infrastructure-as-code, or cloud-native deployments (AWS preferred)". They mention "Experience integrating LLMs, RAG pipelines, or AI APIs". They mention "Experience actively using AI coding tools". They mention "Experience with front-end frameworks such as React, Angular, or Next". They mention "Experience building RESTful APIs and backend services". They mention "Experience with data-intensive applications or data platform integrations". They mention "Experience building and maintaining data ingestion pipelines, ETL workflows, and integrations with the Databricks Lakehouse platform". They mention "Experience with Databricks, or Lakehouse architectures". They mention "Experience working with cloud-native web application development and deployment (AWS, Azure)". They mention "Experience with CI/CD, infrastructure-as-code, or cloud-native deployments (AWS preferred)". They mention "Experience integrating LLMs, RAG pipelines, or AI APIs". They mention "Experience actively using AI coding tools". They mention "Experience with front-end frameworks such as React, Angular, or Next". They mention "Experience building RESTful APIs and backend services". They mention "Experience with data-intensive applications or data platform integrations". They mention "Experience building and maintaining data ingestion pipelines, ETL workflows, and integrations with the Databricks Lakehouse platform". They mention "Experience with Databricks, or Lakehouse architectures". They mention "Experience working with cloud-native web application development and deployment (AWS, Azure)". They mention "Experience with CI/CD, infrastructure-as-code, or cloud-native deployments (AWS preferred)". They mention "Experience integrating LLMs, RAG pipelines, or AI APIs". They mention "Experience actively using AI coding tools". They mention "Experience with front-end frameworks such as React, Angular, or Next". They mention "Experience building RESTful APIs and backend services". They mention "Experience with data-intensive applications or data platform integrations". They mention "Experience building and maintaining data ingestion pipelines, ETL workflows, and integrations with the Databricks Lakehouse platform". They mention "Experience with Databricks, or Lakehouse architectures". They mention "Experience working with cloud-native web application development and deployment (AWS, Azure)". They mention "Experience with CI/CD, infrastructure-as-code, or cloud-native deployments (AWS preferred)". They mention "Experience integrating LLMs, RAG pipelines, or AI APIs". They mention "Experience actively using AI coding tools". They mention "Experience with front-end frameworks such as React, Angular, or Next". They mention "Experience building RESTful APIs and backend services". They mention "Experience with data-intensive applications or data platform integrations". They mention "Experience building and maintaining data ingestion pipelines, ETL workflows, and integrations with the Databricks Lake
    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 RequirementsThe 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.

    Apply