Model ML is the AI workflow builder transforming how major financial institutions produce and validate client-ready work. Model ML converts complex, manual processes into fully automated AI systems that scale across global teams. In under a year, Model ML has become one of the fastest growing enterprise AI platforms worldwide and recently closed a $75 million Series A, one of the largest fintech Series A rounds ever. The round was backed by FT Partners, Y Combinator, LocalGlobe, QED, 13books, and other top global investors.
Job Description
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.
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.
What you can expect
It won't be easy; in fact, it will be very hard.
BUT, it will be a lot of fun.
You need to be comfortable in being uncomfortable; timelines will change, priorities will most likely shift
Be prepared to sacrifice your work-life balance in exchange for joining an incredible journey and learning a lot along the way.
Requirements
5+ years of professional software engineering experience.
Hands-on experience building and deploying AI applications in production environments.
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.
Experience implementing RAG pipelines , AI agent orchestration , and performance monitoring.
Familiarity with LLM evaluation techniques and tools for measuring model accuracy, reliability, and safety.
What We Offer
You will be reporting directly to the founders, who have two successful venture-backed exits under their belt.
Competitive salary + equity
Supportive and innovative work environment
About the interview
Our Process: We're very conscious of everyone's time, so we want to make the process as efficient as possible.
- Call 1: 30-minute intro call with our Talent Acquisition team
- Call 2: 30-minute technical screen
- Call 4: Onsite interview with Engineering Leadership
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