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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