Data Engineer ML

: • Core Software Engineering & Architecture • Cloud, DevOps & Infrastructure • Data Engineering & Governance • Next-Generation AI & Vibe-Coding Practices • Experience: 4-8 years of full-stack software engineering experience in an enterprise or production environment. • Design Patterns: Deep understanding of object-oriented and functional design patterns, with a proven ability to write clean, modular, and testable code. • Polyglot Programming: Proficiency in a diverse set of programming languages: • Python (for data science, modeling, and scripting) • Java (for enterprise-grade backend services) • JavaScript / TypeScript (for full-stack integration and modern web frameworks) • SQL (for complex data querying and relational database management) • Shell Scripting (for system automation and environment configuration) • AWS Ecosystem: Extensive experience deploying, configuring, monitoring, and troubleshooting full-stack applications and ML workloads in Amazon Web Services (AWS) (e.g., SageMaker, ECS/EKS, Lambda, S3, and IAM). • CI/CD Platforms: Hands-on experience orchestrating automated build, test, and deployment pipelines using the GitLab platform. • Data Management: Strong grasp of modern data ingestion, ETL/ELT pipelines, and data persistence strategies, including relational (SQL), non-relational (NoSQL), and search indexing technologies. • Data Security: Solid understanding of best practices for data protection, data minimization, and data classification. • AI-Assisted Tooling: Practical experience utilizing AI-assisted coding platforms (e.g., Cursor, GitHub Copilot, Windsurf) to optimize development velocity. • Vibe-Coding & Context Engineering: Advanced understanding of AI-driven development paradigms ("vibe-coding"), including context window management, prompt structuring, iterative refinement, and the implementation of strict validation guardrails to prevent silent code failures or security vulnerabilities.