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Tier-1 brand, mid-level popular GenAI role, metro location, and high demand increase candidate competition.
Core GenAI skills are transferable, but enterprise controls and compliance add industry-specific constraints.
Explicit 5–7 years plus specialized GenAI, RAG, agentic AI, and infrastructure requirements make shortlisting strict.
Architect and implement generative and agentic AI solutions focusing on advanced context engineering, retrieval-augmented generation, knowledge graphs, and multi-agent orchestration.
Lead design and deployment of scalable, production-ready AI/agentic applications ensuring compliance, observability, and robust governance.
Mentor junior developers and collaborate cross-functionally to deliver grounded AI solutions for operational efficiency improvement.
Bachelor's or master's degree in Computer Science, Data Science, AI, or related field.
5–7 years of AI/software development experience including Generative AI and agentic AI.
Strong hands-on expertise with foundation models, LLMs, RAG systems, knowledge graphs, multi-agent frameworks (Google ADK, LangGraph, etc.), and relevant protocols (MCP, A2A).
Experience with cloud infrastructure (AWS or equivalent), containerization (Docker), orchestration (Kubernetes), and CI/CD pipelines.
Deep technical expertise in architecting production-grade generative AI and multi-agent systems using advanced context engineering and orchestration patterns.
Proven ability to design and integrate complex AI workflows with compliance and operational reliability in large-scale enterprise environments.
Experience mentoring teams and working collaboratively with stakeholders to align AI solutions with business needs.