Data​/AI Engineer

• Design and optimize data models, feature stores, and storage patterns to ensure performance, reliability, and governance across AI workloads
• Build and maintain scalable batch and streaming data pipelines that ingest, transform, and curate high-quality datasets for AI and agentic workflows
• Implement Data Ops and MLOps automation, including CI/CD pipelines, data validation, ML model deployment, monitoring, and drift detection
• Write production-quality code, conduct thorough code reviews, troubleshoot performance issues, and continuously enhance system reliability, scalability, and observability

• Bachelor’s degree in computer science, Software Engineering, or related field, plus experience in data, software, ML, or AI engineering
• Strong SQL and database fundamentals (both relational and No
  
  SQL)
• Proficiency in production-grade coding with strong software engineering fundamentals including version control, testing, and code review practices
• Deep expertise in data modeling, distributed systems, storage optimization, and performance tuning for large-scale AI and analytics workloads
• Solid understanding of the ML data lifecycle, including feature engineering, model integration, deployment support, and monitoring
• Excellent communication and collaboration skills with the ability to translate complex business requirements into well-architected technical solutions; proven ability to work effectively across product, platform, and cross-functional engineering teams

Demonstrates expertise in designing and optimizing data models and pipelines for AI workloads, with a strong foundation in Data Ops and MLOps practices. Proficient in production-grade coding and collaboration across cross-functional teams to deliver high-quality technical solutions.

• Data Modeling
• MLOps Automation
• SQL Proficiency
• Production-Grade Coding
• Data Pipeline Development

• Data Modeling
• SQL
• No
  
  SQL
• Data Ops
• MLOps
• Feature Engineering
• Performance Tuning
• Code Review
• Version Control
• Testing

• Communication
• Collaboration

• AI Workloads
• Data Validation
• ML Model Deployment
• Monitoring
• Drift Detection
• Distributed Systems
• Storage Optimization
• Analytics Workloads