Full Stack AI Engineer

Were looking for a Full Stack AI Engineer to orchestrate AI agents across the entire product lifecycle for a fast-moving enterprise AI platform deployed inside customer infrastructure. You will operate across design, planning, front end, back end, model deployment, and model training — owning the full stack rather than executing a single layer of it.

The hardest thing to find for this role is not technical skill. It is systems thinking. We need someone who can operate at the macro level, understand how all the pieces connect, and make sound architectural decisions across the full product without needing to be handed a narrow task. If you are a strong full stack AI engineer who thinks in systems, can move across the entire product lifecycle independently, and takes genuine ownership of outcomes rather than just deliverables, this role is a strong fit.

• You will be paid in USD (bi-monthly: every 15th and 30th)
• Paid Time Off in accordance with company policy
• Observance of Holidays per company guidelines
• 100% remote setup so you can work wherever youre most productive
• This role requires availability during US business hours
• Work on an enterprise AI platform with documented Fortune 500 deployments and measurable production outcomes
• High-ownership engineering role with direct influence over architecture, product direction, and AI system design

AI Agent Orchestration and System Design

• Design and orchestrate AI agents that proactively surface actions and draft workflows inside customer infrastructure
• Architect multi-agent systems that connect across data sources and operate reliably in production environments
• Build agent logic, tool use, memory systems, and orchestration layers that scale across enterprise deployments
• Think at the system level — understand how every component interacts and make decisions that hold up across the full product

Full Stack Product Development

• Own front end and back end development across the product lifecycle without being limited to a single layer
• Build interfaces, APIs, data pipelines, and infrastructure components as needed to ship complete features
• Move fluidly between design, planning, implementation, and deployment without losing quality at any stage
• Contribute to product architecture decisions from a systems perspective, not just a task execution perspective

Model Deployment and Training

• Deploy and manage AI models inside customer VPC environments with zero data egress requirements
• Support model training workflows that learn from customer-specific historical data
• Monitor model performance in production and iterate based on real outcomes
• Ensure every model deployment meets the companys standard of proving itself on past data before going live

Ownership and Iteration

• Take ambiguous problems and turn them into working systems without requiring detailed specifications
• Ship fast, measure outcomes, and iterate based on production signals
• Identify architectural improvements and technical debt proactively and address them without being asked
• Document systems clearly so the team can build on your work reliably

• Proven full stack engineering experience with the ability to own front end, back end, and infrastructure work simultaneously
• Genuine, production-proven AI and agent engineering experience — building and deploying systems that work at scale, not just in demos
• Systems thinking as a core operating mode — you understand how components connect, you design for the macro, and you make decisions that hold across the full product
• Experience deploying AI models inside enterprise or VPC environments
• Comfortable designing and building multi-agent orchestration systems with tool use, memory, and reliable production behavior
• Strong engineering judgment with the ability to scope, architect, and execute independently
• Excellent written communication for async collaboration across a distributed team

• Experience with AI orchestration frameworks such as LangChain, LlamaIndex, or similar
• Background building AI systems for enterprise clients in regulated or data-sensitive industries
• Experience with model training pipelines and working with customer-specific historical data
• Familiarity with VPC deployment, infrastructure as code, or cloud-native architecture
• Prior experience as a founding engineer or primary engineer on a product where you owned the full stack

Please include:

Only candidates who submit both a portfolio and Loom video will be moved to the next step of the hiring process.

If you are a full stack AI engineer who thinks in systems, can own the entire product lifecycle independently, and wants to build AI infrastructure that proves itself in production at enterprise scale, this role gives you the ownership and the direct impact to do your best work.

Application Process Overview

Our comprehensive selection process ensures we find the right fit for both you and our clients:

Each stage is designed to evaluate your fit for the role while giving you insights into our company culture and expectations. Well keep you informed throughout the process and provide feedback at each step.