Full Stack AI Engineer

Compunnel, Inc.; Los Angeles (CA); On-site; USD 120,000 - 160,000; Full time

Job summary

A technology firm is seeking a Full Stack AI Engineer to design and deliver AI-powered features across a modern platform. The role involves building and managing end-to-end solutions, including workflows, APIs, and UIs. Candidates should have over 5 years of software engineering experience, proficiency in both frontend and backend technologies, and experience with AI-related systems. This position offers a chance to work closely with teams to deliver scalable applications that leverage AI innovations.

Qualifications

• 5+ years of software engineering experience with production application delivery.
• Proficiency in frontend frameworks such as React or Next.js.
• Experience integrating LLMs or AI APIs into applications.

Responsibilities

• Design, build, and maintain modular AI agents.
• Develop and manage RAG pipelines and retrieval architectures.
• Integrate LLMs into user-facing applications.

Skills

Software engineering experience

Agentic AI workflows

Frontend frameworks (React, Next.js)

Backend technologies (Python, Node.js)

RESTful APIs

Relational databases (PostgreSQL)

Containerization and Kubernetes

AI evaluation practices

CI/CD practices

Tools

PostgreSQL

Kubernetes

Job description

We are seeking a Full Stack AI Engineer to design and deliver AI-powered features across a modern platform.

This role involves building end-to-end solutions, including agentic workflows, APIs, and user interfaces that enable AI copilots, predictive tools, and automated pipelines.

The position requires ownership across the full technology stack, from data layer to UI, working closely with cross-functional teams to deliver scalable and intelligent applications.

Key Responsibilities

• Design, build, and maintain modular AI agents that automate multi-step workflows.
• Develop and manage RAG pipelines, retrieval architectures, and semantic search systems.
• Implement evaluation frameworks, guardrails, and human-in-the-loop controls for AI systems.
• Integrate LLMs into user-facing applications and workflows.
• Design and develop scalable full stack applications across frontend and backend.
• Build intuitive AI-driven user interfaces such as chat systems, copilots, and automation tools.
• Own features end-to-end, including data modeling, API development, UI implementation, and deployment.
• Deploy and maintain applications using cloud platforms, containerization, and orchestration tools.
• Ensure reliability, performance, and observability of AI-powered systems in production.
• Collaborate with engineering and data teams to integrate models, pipelines, and external data sources.
• Maintain high code quality through testing, code reviews, and CI/CD practices.
• Rapidly prototype and iterate on AI-driven features based on feedback.
• Contribute to architectural decisions and communicate technical tradeoffs effectively.

Required Qualifications

• 5+ years of software engineering experience with production application delivery.
• Hands-on experience building and managing agentic or multi-step AI workflows.
• Proficiency in frontend frameworks such as React or Next.js and backend technologies such as Python or Node.js.
• Experience integrating LLMs or AI APIs into applications.
• Familiarity with RAG systems, vector databases, and embedding-based retrieval.
• Experience designing and documenting RESTful APIs.
• Proficiency with relational databases such as PostgreSQL and strong SQL skills.
• Knowledge of containerization, Kubernetes, and DevOps practices including CI/CD and observability.
• Experience with AI evaluation practices, including output validation and performance assessment.
• Ability to work independently and own features from concept to deployment.

Preferred Qualifications

• Experience working in startup environments or as an early-stage engineer.
• Portfolio of AI projects such as RAG systems, LLM agents, or copilot-style tools.
• Familiarity with industry-specific domains such as content, rights management, or data-driven platforms.
• Knowledge of data privacy, compliance, and responsible AI practices.