Full Stack AI Engineer

The Friedkin Group•Houston, TX

8d•Hybrid

About The Position

As a Full-Stack AI Engineer, you will design and build enterprise-grade applications that operationalize analytical models, machine learning models, and Gen AI solutions. This includes everything from ingestion pipelines and backend APIs to front-end applications and AI-powered features. You will be the primary builder on cross-functional project pods, working alongside product owners, enterprise data engineers, and data scientists. This role is at the intersection of data engineering, software engineering, AI engineering, and AI science, requiring strong full-stack development skills and the ability to integrate with data pipelines, ML models (via API), and data serving on Databricks. You will leverage AI coding tools (Claude Code, Codex, Augment Code, etc.) to accelerate delivery and maintain a high pace of iteration without sacrificing quality.

Requirements

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.
Bachelor's Degree Computer Science, Software Engineering, Information Systems, or a related technical field
8+ years of experience building production-grade software applications
Strong full-stack development experience (frontend + backend)
Experience building RESTful APIs and backend services
Experience with data-intensive applications or data platform integrations
Experience actively using AI coding tools (Claude Code, Codex, Augment Code) as part of day-to-day development
Experience with front-end frameworks such as React, Angular, or Next
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.

Nice To Haves

Experience working in agile/scrum delivery models with cross-functional teams
Experience integrating LLMs, RAG pipelines, or AI APIs into production applications
Experience with Databricks, or Lakehouse architectures
Experience working with cloud-native web application development and deployment (AWS, Azure)
Experience with CI/CD, infrastructure-as-code, or cloud-native deployments (AWS preferred)

Responsibilities

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.
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.
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.
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.
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.
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.