Senior Full Stack AI Engineer

Walter

About The Position

We are looking for a Senior Full Stack AI Engineer to design, build, and scale production-grade applications powered by generative AI. This role is focused on delivering real products, not just prototypes. The ideal candidate is a strong end-to-end builder who can make sound architectural decisions, integrate AI capabilities into usable systems, and balance fast delivery with long-term maintainability. This is a senior engineering role for someone who can own product development from architecture through deployment. The role requires strong full stack engineering fundamentals combined with hands-on experience integrating generative AI into production workflows. The right candidate understands how to build scalable backend services, intuitive frontends, and reliable AI-enabled features that serve real users. This person should bring strong product thinking, architectural judgment, and the ability to move independently from requirements to shipped solutions.

Requirements

Strong full stack engineering experience, ideally 5+ years
Proficiency in backend development with technologies such as Node.js, Python, or similar
Strong frontend experience with frameworks such as React or comparable modern tools
Experience building and consuming APIs, working with databases, and deploying on cloud platforms
Hands-on experience integrating generative AI into production applications
Practical understanding of prompt engineering and AI system limitations, including failure modes and edge cases
Proven ability to design systems that scale beyond MVP and remain maintainable over time
Strong understanding of software architecture, system design, and engineering best practices
Demonstrated experience shipping multiple production-grade products or platforms
Ability to combine traditional software engineering discipline with modern AI-driven workflows
Experience working independently with strong ownership and product mindset

Responsibilities

Build end-to-end applications that incorporate generative AI capabilities into real user workflows
Develop scalable backend services, APIs, and integrations using modern engineering practices
Build responsive and maintainable frontend experiences using modern frameworks
Design modular system architectures that support scalability, reliability, and long-term maintainability
Integrate LLMs and other AI tools into production applications with appropriate safeguards and monitoring
Optimize prompt flows, data pipelines, and system interactions for consistency and performance
Translate business requirements into technical solutions and make strong implementation trade-offs
Own features from ideation through development, testing, deployment, and iteration
Reuse and adapt proven solutions where appropriate to accelerate delivery
Continuously improve product quality based on performance data, feedback, and operational learnings