AI Data Engineer

• Python Working understanding of how GenAI systems consume data training datasets, features and labels, embeddings, retrieval and grounding data, evaluation harnesses, and the data-side failure modes that drive model and agent regressions.
• Hands-on experience using AI-assisted development tools (e.g., GitHub Copilot, agentic coding workflows, GenAI-based code review and test generation) in a disciplined, production-grade way.
• Experience integrating AI capabilities (LLMs, agents, model-backed features) into production systems, including familiarity with Responsible AI principles and applying AI safety controls in production.
• Experience owning a feature area end-to-end, from design through deployment, monitoring, and on-call ownership.
• Experience designing and operating large-scale distributed data systems in a cloud environment (e.g., Azure), including data model and pipeline design, performance tuning, and cost optimization.
• Engineering fundamentals: data structures and algorithms, object-oriented and systems design, and building resilient services (reliability, availability, scalability).
• Experience with DevOps practices and tooling (CI/CD, infrastructure as code, monitoring and alerting, incident response) and a track record of driving toward zero-touch deployment.

Location : - Remote