Applied AI Engineer

About the role

Model ML is an AI-native workspace initially designed for investment banking, private equity, consultancies, and asset managers. You will work alongside two well-known founders who successfully sold their last two YC-backed companies. Model ML, has already raised double figures from Y Combinator and top-tier venture firms. With strong product-market fit and live production deployments at some of the world's largest and most prestigious institutions, Model ML's team is working around the clock across offices in New York, London, and Hong Kong to become the leading AI application platform for enterprise.

Soon, all your digital information will be stored in one place. Everything you do, whether that be sending emails, building presentations, having meetings, pulling data, or doing research, will all be underpinned by an AI model that belongs to you. Using Model ML, you'll simply talk to your laptop or phone to get things done.

About the role

In this role, you will own and drive large portions of our AI agent infrastructure, from designing and deploying multi-agent systems to integrating Retrieval-Augmented Generation (RAG) pipelines, and evaluation frameworks. You will be responsible for delivering AI-powered features into production at scale — ensuring they are performant, reliable, and secure — while also contributing across the stack, from frontend interfaces to backend APIs, databases, and deployment pipelines.

Key Responsibilities

- Build, test, and deploy backend services and APIs (Python/Django/FastAPI preferred, but other languages/frameworks welcome).

- Collaborate with founders, growth team, designers, and other engineers to deliver high-impact features.

- Ensure scalability, performance, and security across the stack.

- Develop and deploy AI-powered features in production, including RAG (Retrieval-Augmented Generation) systems, multi-agent infrastructure, and evaluation frameworks (Evals).

- Create data pipelines for AI model training, evaluation, and continuous improvement.

- Mentor junior developers and promote engineering best practices.

Required Qualifications

- 5+ years of professional software engineering experience.

- Hands-on experience building and deploying AI applications in production environments.

- Strong backend development skills (Python preferred)

- Solid understanding of relational databases.

- Experience with Git and collaborative development workflows.

- Knowledge of cloud infrastructure, containerization (Docker, Kubernetes), and CI/CD pipelines.

- Strong problem-solving skills and a passion for building great products.

- Experience implementing background workers and task queues (Celery, RQ, etc.).

- Proficiency with Redis for caching, pub/sub, or job queues.

- Hands-on experience building and deploying AI applications in production environments.