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 DataOps 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

Requirements

• 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 NoSQL)


• 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

Core Competencies

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

Highest-signal resume keywords

• Data Modeling


• MLOps Automation


• SQL Proficiency


• Production-Grade Coding


• Data Pipeline Development

Hard Skills

• Data Modeling


• SQL


• NoSQL


• DataOps


• MLOps


• Feature Engineering


• Performance Tuning


• Code Review


• Version Control


• Testing

Soft Skills

• Communication


• Collaboration

Industry Keywords

• AI Workloads


• Data Validation


• ML Model Deployment


• Monitoring


• Drift Detection


• Distributed Systems


• Storage Optimization


• Analytics Workloads

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