Member of Technical Staff - AI

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. Job 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. 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.