Staff ML Engineer

Summary

Join Oportun, a mission-driven fintech company, as a Staff ML Engineer and build self-serve platforms combining real-time ML deployment and advanced data engineering. Design and build platforms supporting real-time ML deployment and robust data engineering workflows. Develop microservices-based solutions using Kubernetes and Docker. Create APIs and backend services using Python and FastAPI. Architect and implement platforms for real-time ML inference using AWS SageMaker and Databricks. Build and optimize ETL/ELT pipelines using PySpark and Pandas. Design scalable, distributed data pipelines on AWS, integrating various tools. Implement data lake and data warehouse solutions. Design and implement robust CI/CD pipelines using Jenkins and GitHub Actions.

Requirements

• 10-15 years of experience in platform engineering, backend engineering, DevOps, or data engineering roles
• 5 years experience as architect building platforms that scale
• Hands-on experience with real-time ML model deployment and data engineering workflows
• Strong expertise in core Python and experience with Pandas, PySpark, and FastAPI
• Proficiency in container orchestration tools such as Kubernetes (K8s) and Docker
• Advanced knowledge of AWS services like SageMaker, Lambda, DynamoDB, EC2, and S3
• Proven experience building and optimizing distributed data pipelines using Databricks and PySpark
• Solid understanding of databases such as MongoDB, DynamoDB, MariaDB, and PostgreSQL
• Proficiency with CI/CD tools like Jenkins, GitHub Actions, and related automation frameworks
• Hands-on experience with observability tools like New Relic for monitoring and troubleshooting

Responsibilities

• Design and build self-serve platforms that support real-time ML deployment and robust data engineering workflows
• Develop microservices-based solutions using Kubernetes and Docker for scalability, fault tolerance, and efficiency
• Create APIs and backend services using Python and FastAPI to manage and monitor ML workflows and data pipelines
• Architect and implement platforms for real-time ML inference using tools like AWS SageMaker and Databricks
• Enable model versioning, monitoring, and lifecycle management with observability tools such as New Relic
• Build and optimize ETL/ELT pipelines for data preprocessing, transformation, and storage using PySpark and Pandas
• Develop and manage feature stores to ensure consistent, high-quality data for ML model training and deployment
• Design scalable, distributed data pipelines on platforms like AWS, integrating tools such as DynamoDB, PostgreSQL, MongoDB, and MariaDB
• Implement data lake and data warehouse solutions to support advanced analytics and ML workflows
• Design and implement robust CI/CD pipelines using Jenkins, GitHub Actions, and other tools for automated deployments and testing
• Automate data validation and monitoring processes to ensure high-quality and consistent data workflows
• Create and maintain detailed technical documentation, including high-level and low-level architecture designs
• Collaborate with cross-functional teams to gather requirements and deliver solutions that align with business goals
• Participate in Agile processes such as sprint planning, daily standups, and retrospectives using tools like Jira