About the role

We’re looking for a Machine Learning Lead Engineer who wants to operate as a player-coach — someone still very hands-on, but excited to grow into a proper management role over time. This is not a pure manager role from day one. You’ll be building, shipping, and improving ML systems while gradually taking on more team leadership responsibilities.

Key Responsibilities

What you’ll do:

Build and maintain production-grade ML systems and pipelines
Stay hands-on (coding, reviews, debugging, deployments)
Mentor a small team of ML engineers (2–3 people initially)
Establish ML engineering & MLOps best practices
Improve deployment, monitoring, and model lifecycle management
Collaborate with DS, Risk, Product, and Engineering
Help shape roadmap and hiring

Skills, Knowledge & Expertise

Strong background as an ML Engineer (not purely DS/research)
Experience deploying and running ML models in production
Solid software engineering skills (Python, APIs, systems)
Good understanding of MLOps stack (CI/CD, monitoring, pipelines)
Familiarity with tools like MLflow, Airflow, Evidently, DVC, Docker, Kubernetes.
Cloud experience (AWS/GCP/Azure)
Some mentoring/leadership experience + desire to grow into management
Nice to have:
- Experience scaling ML platforms or teams
- Fintech / lending / risk domain exposure