MLOps / Data Engineer

Are you passionate about building scalable data pipelines and optimizing machine learning operations? We are looking for an experienced MLOps / Data Engineer to help design, build, and maintain our risk model infrastructure and data pipelines. Youll play a crucial role in transforming raw data into a structured, optimized format for training and inference, ensuring accuracy, efficiency, and scalability.

What youll do:

• Develop and optimize data pipelines for risk model ingestion, processing, and storage.
• Build scalable model training, deployment, and monitoring pipelines using cloud technologies (AWS, GCP).
• Expose models as pickles for risk service consumption and work closely with the risk team to monitor model performance.
• Implement data quality and governance frameworks to ensure accurate and reliable data.
• Collaborate with data analysts, scientists, and engineers to deliver insights and improve business decision-making.
• Optimize query performance and storage solutions for large-scale datasets.
• Research and implement new MLOps best practices to enhance efficiency and scalability.

What were looking for:

• 3+ years of experience as an MLOps Engineer or Data Engineer (with at least 1 year in the other role).
• Proven expertise in designing and implementing data pipelines and systems.
• Strong programming skills in Python, Java, or Scala.
• Experience with ETL tools (e.g., Apache Airflow, dbt) and relational & NoSQL databases (PostgreSQL, MySQL, Snowflake).
• Hands-on experience with cloud-based data solutions (AWS Redshift, Google BigQuery, Snowflake).
• Experience with SageMaker and Feature Store (preferred).
• Knowledge of distributed data systems (Spark, Kafka) is a plus.
• Strong problem-solving skills and ability to work in a fast-paced startup environment.
• Excellent communication and collaboration skills to work with both technical and non-technical teams.