MLOps Engineer
Core
Support product teams in adopting data engineering and MLOps best practices to build scalable, efficient, and reliable data and ML solutions.
Role type
MLOps Engineer
Builds
Scalable data pipelines, model deployment workflows, monitoring strategies, and cost-efficient practices for financial AI products.
Domain
Fintech / Machine Learning Operations
Deliverable
production ML models | infrastructure
Required skills
Data system design, Python, distributed processing frameworks (PySpark, Flink), containerization (Docker, Kubernetes), Infrastructure as Code (Terraform), SQL/NoSQL storage management, cross-functional collaboration.
Preferred skills
Streaming platforms (stream > table, table > stream), core data structures, monitoring and alerting, API deployment, Feature Stores, ML pipeline tools (Kubeflow, MLflow, Airflow, Flyte).
Technologies
Python, PySpark, Flink, Docker, Kubernetes, Terraform, PostgreSQL, AWS, Heroku, React Native, TypeScript, Ruby on Rails, Minitest, CircleCI.
Responsibilities
Champion best practices in data engineering and MLOps; implement robust data pipelines and model deployment workflows; act as a bridge between product teams and the Data Platform team to improve internal tooling and infrastructure.
Seniority
Mid-level, hands-on IC