Data Engineer - Applied AI - Data Cycling Center

About the team
The Machine Learning Engineering (MLE) team focused on the application of multimodal LLMs, unsupervised learning, and clustering algorithms. The ideal candidate will work closely with product, operations, and engineering teams to apply advanced natural language processing, computer vision, and deep learning technologies to solve business challenges and extract actionable insights.

Responsibilities
- Collaborate closely with data scientists, machine learning engineers, and software developers to build scalable data pipelines.
- Support the development and deployment of machine learning models by ensuring high-quality, reliable data infrastructure.
- Drive innovation in data processing, storage, and retrieval to optimize model training and inference.
- Work in an agile environment focused on continuous integration and delivery of ML solutions.
- Contribute to the design and implementation of data governance and security best practices.

Minimum Qualifications:
- Bachelor’s degree in Computer Science, Engineering, or a related field along with proven experience in data engineering, preferably within a machine learning or AI-focused team.
- Strong proficiency in programming languages such as Python, Java, or Scala.
- Hands-on experience with big data technologies (e.g., Hadoop, Spark, Kafka) along with strong expertise in designing, building, and maintaining ETL/ELT pipelines.
- Familiarity with cloud platforms (AWS, GCP, or Azure) and their data services.

Preferred Qualifications
- Solid understanding of database systems, both SQL and NoSQL.
- Experience optimizing performance in big data and fully understanding data skew along with familiarity with data governance, lineage, and real-time data processing practices.
- Knowledge of machine learning workflows and data requirements along with excellent problem-solving skills and ability to work collaboratively in cross-functional teams.