Model ML
Member of Technical Staff - AI
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Member of Technical Staff - AI
Company Overview:
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, bringing total funding to $90 million.
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.
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Job Responsibilities
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- 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.
Job Requirements
- 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.
- Experience implementing RAG pipelines, AI agent orchestration, and performance monitoring.
- Familiarity with LLM evaluation techniques and tools for measuring model accuracy, reliability, and safety.
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