We are looking for a hands-on, ownership-driven AI Data Engineer to play a key role in building and scaling the data infrastructure behind an advanced AI engine (Large Market Model – LMM).

This position sits at the intersection of Data Engineering, Machine Learning, and MLOps. You will design and operate production-grade data and modeling pipelines that transform large-scale, complex datasets into predictive intelligence.

Responsibilities

• Design and build scalable, AI-ready data pipelines ingesting diverse structured and unstructured datasets

• Develop advanced feature engineering pipelines in collaboration with domain experts

• Implement and maintain distributed data processing systems (e.g., Spark, Dask)

• Build robust data validation and model evaluation frameworks

• Monitor production systems for model performance, reliability, and data drift

• Own CI/CD processes for ML pipelines and deployments

• Optimize end-to-end ML workflows from ingestion to serving

• Continuously research and experiment with improvements in feature engineering, MLOps, and model optimization

Requirements

• 2+ years of commercial experience building production-level AI/ML data systems

• Strong proficiency in Python and the ML/data ecosystem (Pandas, Scikit-learn)

• Experience with distributed computing frameworks (Spark, Dask, etc.)

• Hands-on experience designing and maintaining SQL-based ETL pipelines

• Practical experience with MLOps principles and orchestration tools (e.g., Airflow, Dagster)

• Solid software engineering fundamentals

• Experience with containerization (Docker)

• Experience working with cloud platforms (AWS, GCP, or Azure)

• Strong problem-solving and communication skills

• B.Sc. or M.Sc. in Computer Science, Engineering, Mathematics, or a related quantitative field

Nice to Have

• Experience in dynamic pricing or other high-frequency, data-intensive environments

• Contributions to open-source ML or data engineering projects