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Tier-1 employer and Bengaluru location increase competition, niche agentic AI requirements moderate applicant density.
Core GenAI, RAG, and agentic AI skills transfer across industries, but financial compliance experience raises domain specificity.
Explicit 8–10 years and deep, mandatory agentic AI, RAG, vector DB, MLOps, and Kubernetes skills make filters stringent.
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Own end-to-end architecture and hands-on implementation of enterprise-grade AI solutions focusing on autonomous agents and agentic AI frameworks (e.g., Google ADK).
Develop and lead advanced frameworks for AgentOps and AI governance including evaluation, post-production observability, and traceability.
Partner with product leadership and cross-functional teams (Technology, MRM, Legal, Compliance, Business) to shape AI product strategy and ensure compliance and alignment with enterprise goals.
8–10 years of professional experience in software engineering with significant focus on building and deploying large-scale AI/ML systems.
Strong expertise in Python and architecting complex systems using agentic frameworks such as Google ADK, LangChain, AutoGen.
Bachelor's degree in Computer Science or related field; Master's degree highly preferred.
Proven experience in context optimization, knowledge storage (vector databases, knowledge graphs), Retrieval-Augmented Generation (RAG), scalable backend API design, and evaluation strategies (e.g., LangFuse).
Senior individual contributor capable of leading challenging technical problems in AI with deep hands-on expertise in autonomous agents and agentic AI architectures.
Experience working in complex regulated environments, preferably with familiarity in financial services and coordination with Legal, Compliance, and Model Risk Management.
Strong systems architect with background in distributed systems, scalable backend APIs, AI evaluation, and governance frameworks, combined with knowledge of Kubernetes and MLOps principles.