Senior Software ML Engineer (R12389)

Position Overview:

We are seeking a highly skilled and experienced Senior Machine Learning Engineer to join our dynamic team and lead the development of our ML infrastructure, from model training to deployment, enabling us to deliver advanced and impactful solutions to our clients. As the Senior Machine Learning Engineer at Oportun, you will assume a pivotal role in elevating our ML capabilities, responsible for conceiving and implementing a state-of-the-art machine learning infrastructure. Your mastery of a ML domain enables you to take up business problems and solve them with a AI/ML solution. With your depth of expertise and leadership abilities, you will actively contribute to architectural decisions, mentor junior ML engineers, and collaborate closely with diverse teams, including data scientists, and engineers, to deliver high-quality Ml solutions that redefine the norms of FinTech. Your profound expertise in architecting and deploying machine learning models will be instrumental in propelling our products to new dimensions of sophistication and success. This is a role where you will have the opportunity to take up responsibility in leading the technology effort – from technical requirements gathering to final successful delivery of the ML solution - for large initiatives (cross-functional and multi-month long projects).

Responsibilities:

ML Infrastructure Development:

1. Design and implement scalable ML pipelines using Databricks, PySpark, AWS SageMaker, and Python to support model training, testing, and deployment.
2. Leverage FastAPI for building and deploying lightweight, high-performance RESTful APIs for model serving.
3. Utilize Kubernetes and Docker for containerization and orchestration to ensure fault-tolerant and distributed ML workflows.
4. Integrate with databases like MongoDB, MariaDB, and DynamoDB for efficient data storage and retrieval.

Feature Engineering and Data Pipelines:

1. Develop and optimize real-time and batch feature pipelines using PySpark on Databricks to handle large-scale data processing.
2. Ensure smooth data integration across NoSQL (MongoDB, DynamoDB) and SQL (MariaDB) databases.

Model Deployment and Monitoring:

1. Deploy ML models in production using AWS SageMaker or FastAPI for API-based deployments, ensuring high performance and low latency.
2. Set up monitoring and alerting with tools like New Relic to ensure the reliability of deployed models.

Collaboration and Mentorship:

1. Work closely with data scientists to transition research-grade models into scalable production systems.
2. Mentor junior engineers on best practices in ML development, FastAPI, and scalable deployment strategies.

CI/CD and Automation:

1. Build and maintain automated CI/CD pipelines using Jenkins and Docker, ensuring smooth integration and deployment of ML workflows.
2. Automate retraining pipelines to ensure models adapt to changing data and maintain performance.

Qualifications:

Experience: 5+ years in ML system design and deployment, with hands-on expertise in Databricks, PySpark, AWS SageMaker, and FastAPI.

Technical Skills:

1. Strong proficiency in Python, PySpark, and cloud services like AWS, S3, DynamoDB, and SageMaker.
2. Experience with containerization (Docker) and orchestration (Kubernetes).
3. Familiarity with monitoring tools like New Relic and databases like MongoDB, MariaDB, and DynamoDB.

Other Attributes:

A tech-agnostic mindset with the ability to adapt to new tools and frameworks.
Strong problem-solving and collaboration skills