Senior Fullstack Engineer - AI

Harnham; Houston (TX); On-site; USD 130,000 - 180,000; Full time

Job summary

Harnham is seeking an experienced Full Stack AI Engineer to design, develop, and deploy production-grade apps that blend modern software engineering with AI capabilities. You will own solutions across UI, APIs, data pipelines, and AI features, collaborating with product, data, and engineering teams.

You will build dashboards, semantic search, document processing, and workflow automation, integrating LLMs and RAG in enterprise environments.

Qualifications

• Bachelor's degree in Computer Science, Software Engineering, Information Systems, or related technical discipline.
• 8+ years of professional software engineering experience.
• Minimum 5+ years of hands‑on full stack development experience.
• Recent experience applying AI technologies within software products, including 2+ years working with practical AI implementations.
• Experience with React, Angular, or similar frontend frameworks and Python backends (FastAPI/Flask/Django).
• Experience with RESTful APIs, data pipelines, and cloud-native apps (GCP preferred).

Responsibilities

• Design and implement end-to-end applications spanning frontend, backend, data, and AI layers.
• Contribute to architecture decisions, technical reviews, and engineering standards.
• Develop scalable, secure, and maintainable systems for enterprise environments.
• Create patterns and frameworks for AI-enabled application development.
• Build dashboards and data-driven interfaces for business users.
• Integrate LLMs, RAG, AI services, and AI-assisted coding tools into production apps.
• Collaborate with data and ML teams to operationalise AI capabilities.

Skills

React

Angular

Python

FastAPI

Flask

Django

RESTful APIs

GCP

RAG

LLMs

Data pipelines

CI/CD

UI/UX design

Education

Bachelor's degree in Computer Science or related field

Tools

CI/CD tooling

Job description

A fast-growing organisation is seeking an experienced Full Stack AI Engineer to design, develop, and deploy production-grade applications that combine modern software engineering with practical AI capabilities. This role is ideal for a software engineer who has evolved into AI-focused development and has hands-on experience integrating large language models, generative AI solutions, retrieval systems, and intelligent workflows into enterprise applications.

The successful candidate will own solutions across the full technology stack, from user interfaces and APIs to data pipelines and AI-enabled features. You'll work closely with product, data, and engineering stakeholders to deliver scalable, business-focused solutions in a collaborative environment.

• Design and implement end-to-end applications spanning frontend, backend, data, and AI layers.
• Contribute to architecture decisions, technical reviews, and engineering standards.
• Develop scalable, secure, and maintainable systems suitable for enterprise environments.
• Create patterns and frameworks for AI-enabled application development.

Full Stack Development

• Build user-facing web applications, APIs, workflows, and data-driven solutions.
• Translate analytical and AI outputs into intuitive user experiences.
• Develop applications using modern frontend frameworks and Python-based backend technologies.
• Deliver responsive dashboards, data applications, and self-service tools that support business users.
• Implement AI-powered features such as conversational interfaces, semantic search, document processing, and workflow automation.
• Integrate LLMs, retrieval‑augmented generation (RAG), agent‑based systems, and AI services into production applications.
• Evaluate and select appropriate AI tools, frameworks, and platforms.
• Collaborate with data and machine learning teams to operationalise AI capabilities.

Data & Platform Integration

• Build and maintain data ingestion pipelines and integration workflows.
• Connect applications to data platforms, model endpoints, and enterprise data sources.
• Ensure data quality, reliability, scalability, and performance.
• Support AI memory systems, knowledge retrieval mechanisms, and data synchronisation processes.
• Leverage AI-assisted development tools as part of daily engineering workflows.
• Implement CI/CD pipelines, automated testing, monitoring, and observability practices.
• Participate in code reviews and contribute to engineering best practices.
• Maintain high standards for security, documentation, testing, and software quality.

Required Qualifications

• Bachelor's degree in Computer Science, Software Engineering, Information Systems, or a related technical discipline.
• 8+ years of professional software engineering experience.
• Minimum 5+ years of hands‑on full stack development experience.
• Recent experience applying AI technologies within software products, including at least 2+ years working with practical AI implementations.
• Strong understanding of:
• Generative AI applications
• Agentic systems and AI workflows
• Retrieval-Augmented Generation (RAG)
• Experience building RESTful APIs and backend services.
• Strong frontend development experience using frameworks such as React, Angular, or similar technologies.
• Strong backend development experience using Python frameworks such as FastAPI, Flask, or Django.
• Experience working with cloud-native applications, with a preference for Google Cloud Platform (GCP).
• Experience with data-intensive applications and platform integrations.
• Hands‑on use of AI-assisted coding tools within the software development lifecycle.
• Strong communication, collaboration, and stakeholder engagement skills.
• Experience operating in fast‑paced environments with evolving requirements.

Preferred Qualifications

• Experience integrating LLMs, RAG frameworks, AI APIs, and intelligent agents into production environments.
• Experience with modern data platforms, lakehouse architectures, or large-scale data ecosystems.
• Background working in startup, scale‑up, or small‑to‑mid‑sized engineering teams.
• Experience with infrastructure‑as‑code, CI/CD automation, and cloud deployment practices.
• Familiarity with agile software delivery methodologies.
• Experience owning products and systems throughout their full lifecycle.
• Strong product mindset with a focus on usability and business impact.