• Design and develop scalable data pipelines for ingesting, transforming, and storing large, diverse data sources.
• Collaborate with AI/ML engineers to prepare and manage datasets for model training, fine-tuning, and deployment.
• Architect and optimize data infrastructure across distributed environments (AWS, GCP, or Azure).
• Implement robust data governance, lineage, and monitoring frameworks to ensure high data quality and reliability.
• Partner with backend engineers to integrate data systems with the AI Assistant core platform.
• Enforce data security, compliance, and performance best practices across the data lifecycle.

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related field.
• 5+ years of experience in data engineering or big data systems.
• Proficiency in Python or Java for data processing and automation.
• Hands-on experience with distributed data frameworks (Spark, Kafka, Airflow, or similar).
• Strong understanding of data modeling, ETL pipelines, and both SQL and NoSQL databases.
• Experience with cloud-based data services (AWS Glue, BigQuery, Azure Data Factory, etc.).
• Familiarity with MLOps, data versioning, or model deployment pipelines is a strong plus.
• Excellent analytical, problem-solving, and communication skills.

Preferred Qualifications

• Experience working with AI or NLP systems.
  
• Familiarity with vector databases, embeddings, and LLM fine-tuning workflows.
  
• Knowledge of containerization and orchestration tools (Docker, Kubernetes).
• Ability to work cross-functionally in Agile or fast-paced R&D environments.