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Tier-1 bank brand and metro location increase applicant density despite niche agentic AI specialization.
Highly specialized agentic AI and model-operationalization expertise limits cross-industry transferability.
Multiple mandatory senior-level AI, agentic framework, and production ML/AI operational requirements enforce strict filtering.
Design, develop, deploy, operate, and continuously improve production-grade AI-native services, agent systems, APIs, automation services, and cloud-native components.
Architect and govern agentic AI-enabled engineering workflows to improve delivery speed, code quality, and operational resilience at scale, ensuring security and compliance guardrails.
Own observability, testing, telemetry, incident response, performance tuning, and cost optimization of AI and data engineering workloads.
7+ years of applied experience with formal training or certification in software engineering.
Hands-on mastery in agentic development using Google ADK, MCP, or equivalent enterprise-grade agent frameworks.
Proficiency in Python and practical experience with AI/ML engineering frameworks such as LangChain, LlamaIndex, PyTorch, or Hugging Face.
Experience personally architecting, deploying, and maintaining production AI systems with secure tool exposure, validation, and risk governance.
Experienced in leading adoption of agentic AI-enabled development practices and setting enterprise standards for validation, auditability, and secure data handling.
Strong domain expertise in responsible AI engineering practices, including security, resiliency, data sensitivity, and risk-based governance at scale.
Operationally skilled in AI production monitoring, prompt engineering, multi-agent system design, and cross-team stakeholder collaboration for delivering reliable enterprise AI solutions.