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 setting (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

📌 Ai Data Engineer Hyderabad (India)
🏢 Launch IT Consulting
📍 India