Job Description

You are a seasoned data engineering professional who is passionate about building scalable data solutions and inspiring others to learn more. You want to join a fast-moving company and shape our architecture to help us scale. You have an agile mindset and want to deliver value incrementally. You'll be part of the data leadership team and work closely with our analytics, data science and governance teams in order to deliver data solutions that generate business value.

Responsibilities:

• Creating and maintaining ML pipelines to operationalize ML models.
• Developing & deploying low latency and highly scalable dockerized microservices.
• Collaborating in cross-functional software/architecture design sessions to find the best solutions for the problems that we are facing.
• Working with Peer ML engineers who will be responsible for scaling and deploying machine learning models for Tide.
• Participating in an agile development team that delivers value iteratively.
• Building ML platform to speed up the development & deploy cycle and monitoring of models in production.

Requirements:

• You have at least 7+ years of development experience
• You have experience leading a team of backend developers and/or ML engineers, coaching best practices, and architecting solutions.
• You have extensive development experience in Python, including the development of microservices using e. g. Flask, Django, etc.
• You have experience in building data solutions, both batch processes and streaming applications.
• You are familiar with event-driven designs, specifically, you have worked with Kafka, Pulsar, RabbitMQ, etc. before.
• You have experience working in an agile team, dedicated to generating value in an iterative fashion.
• You have worked with feature store, ML Observability and automated MLOps systems
• Experience in batch processing frameworks.
• You have high development standards, especially for code quality, code reviews, unit testing, continuous integration and deployment.
• You have experience working with machine learning models before and know about the challenges faced when putting these into production.
• Business-level of English and good communication skills.
• Experience with Git and Docker.
• Experience working with ML platforms is a plus.