Strong hands-on experience (10-12 years) in Python or Java (mandatory).
Proven experience designing and delivering LLM / GenAI solutions (e.g., RAG, orchestration, prompt engineering, AI-assisted automation).
Solid understanding of microservices, APIs, distributed systems, and enterprise integration patterns.
Experience reviewing and governing AI-assisted / GenAI-generated code.
Strong foundation in secure coding practices and performance optimizatio
AI & GenAI Experience designing and implementing LLM-based solutions (RAG, agents, copilots, automation).
Understanding of model lifecycle, inference pipelines, performance tuning, and cost optimization.
Familiarity with AI governance concepts such as bias, explainability, auditability, and model risk controls.
Experience with cloud-native or hybrid environments, containers, and orchestration platforms.
Exposure to CI/CD pipelines, observability, and production support models.
Knowledge of secure coding practices and enterprise security standards.
