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.
  
  

• 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.