Senior Full-stack AI Engineer

As our Full-Stack AI Engineer, you'll turn promising ideas and early-stage prototypes into reliable, production-ready applications. You'll work across the entire stack, from user interface and backend architecture to AI models, integrations, cloud infrastructure, and monitoring.

This is a role for someone who wants ownership from day one. You'll help identify the right problems to solve, decide what should be kept, improved, or rebuilt, and ship intelligent tools that teams can depend on at scale.

The Role

You'll own the end-to-end development of AI-powered products, from opportunity identification and technical scoping to deployment, adoption, and continuous improvement.

You'll combine full-stack product engineering with applied AI, building intuitive applications around technologies such as NLP, LLMs, RAG, agentic workflows, and automation. You'll work closely with Product and business stakeholders to translate complex needs into focused, scalable solutions with clear business value.

Some solutions may begin as loosely built prototypes. Your role is to give them the architecture, reliability, security, and usability they need to succeed in production. You'll balance speed with quality, making pragmatic decisions that move the product forward without adding unnecessary complexity., * Build and maintain AI-powered applications across frontend, backend, data, models, and infrastructure.

• Turn experimental tools and prototypes into secure, scalable, and maintainable production systems.
• Develop intuitive user experiences using React and modern frontend technologies.
• Build backend services, APIs, and workflows using Python and frameworks such as FastAPI, Firebase, Streamlit, or similar technologies.
• Design and implement AI solutions using NLP, machine learning, LLMs, RAG, tool use, agentic workflows, and multimodal systems.
• Create integrations through APIs and webhooks, connecting AI products with Loop's internal platforms and workflows.
• Work with databases such as PostgreSQL, Supabase, or comparable technologies to build reliable data foundations.
• Set up and manage cloud infrastructure, deployments, CI/CD pipelines, monitoring, and observability.
• Apply strong DevOps and software engineering practices to monitor, improve, and scale systems in production.
• Ensure applications meet appropriate security, privacy, and data-handling standards.
• Identify performance bottlenecks and improve system scalability, stability, and reliability.
• Partner with Product and stakeholders to define problems, challenge assumptions, and agree on measurable success criteria.
• Define technical standards and help improve software development practices across Loop.

You Won't

• Deliver impressive demos without taking responsibility for production follow-through.
• Build AI systems without clear adoption, performance, or business-impact metrics.
• Overengineer solutions that should remain simple.
• Treat AI as separate from the product experience around it.
• Wait for perfect requirements or endless approvals before moving forward.
• Hide behind process when a clear technical decision needs to be made.
• Avoid accountability for architecture, implementation, or production outcomes.
• Cruise quietly in the background while others determine the direction.

How You'll Succeed

• You take full ownership of what you build, from the first conversation about a business problem to the moment a reliable solution is being used in production.
• You bring structure to ambiguity and turn loosely defined needs into clear technical decisions, practical roadmaps, and focused products.
• You understand that a strong AI workflow is only one part of a successful product. You pay equal attention to usability, architecture, data quality, integrations, security, performance, and adoption.
• You move between experimentation and production with discipline, knowing when to test quickly and when to invest in long-term quality.
• You balance speed, quality, and complexity, choosing the simplest solution that can deliver meaningful and sustainable impact.
• You communicate technical concepts clearly, align stakeholders around trade-offs, and stay close to Product when priorities or requirements change.
• You measure success through real usage, system performance, time saved, improved decision-making, and efficiency gained.

Experience: 5+ years in software engineering, including hands-on experience shipping AI-powered products, * 5+ years of experience in software engineering, with strong hands-on experience across full-stack product development.

• Production experience building and deploying AI- or machine-learning-powered applications.
• Advanced proficiency in Python and experience with frameworks such as FastAPI, PyTorch, and scikit-learn.
• Strong experience with React and modern frontend development.
• Hands-on experience with, LLMs, and modern generative AI approaches, including RAG, agentic workflows, tool use, and multi-agentsystems.
• Experience with model evaluation, data preprocessing, performance optimization, and production monitoring.
• A solid understanding of system design, software architecture, APIs, databases, and scalable application development.
• Experience with PostgreSQL, Supabase, Firebase, or comparable data platforms.
• Hands-on experience with cloud infrastructure, Docker, CI/CD, and infrastructure-as-code tools such as Terraform.
• Experience with Kubernetes and cloud platforms such as Azure or Google Cloud is a strong advantage.
• The ability to independently deliver an end-to-end solution across frontend, backend, AI, data, and infrastructure.
• A pragmatic mindset, balancing technical ambition with usability, speed, and measurable business value.
• A degree in Computer Science or a related technical field is valuable, but equivalent practical experience matters just as much.

Loop isn't for everyone. We might not be a match if you:

Wait for direction instead of taking initiative.

Prefer experimentation without production responsibility.

Mistake technical activity for customer or business impact.

Feel uncomfortable when the problem, product, or architecture is still taking shape.

Struggle to connect AI capabilities to practical, measurable outcomes.

Prefer alignment meetings over making informed decisions.

Avoid setting technical direction or challenging unclear requirements.

Still listening?

Good. We're not looking for someone to build AI demos from the sidelines. We're looking for someone who can turn ambitious ideas into intelligent products that work, scale, and reshape how teams operate.

At Loop, we see AI as more than a collection of experiments. Used well, it can transform how teams make decisions, create assets, understand customers, and get work done.