AI Agentic Lead

Company DescriptionPandita AI Inc is a leading innovator in the field of machine learning, dedicated to creating cutting-edge products that redefine how industries and individuals leverage data and artificial intelligence technologies.Role OverviewThe AI Agentic Lead will be responsible for architecting, building, and scaling agentic AI systems that autonomously plan, reason, and execute complex workflows. This role will lead the development of multi-agent architectures aligned with product strategy and enterprise use cases.Key Responsibilities- Lead the design and implementation of agentic AI systems using LLMs, tools, memory, planning, and orchestration frameworks. - Define agent behaviors, decision-making logic, and evaluation mechanisms for production-grade systems. - Collaborate with product, engineering, and business teams to translate requirements into autonomous AI workflows. - Drive model selection, prompt strategies, performance optimization, and continuous improvement. - Ensure scalability, security, governance, and ethical use of agent-based AI systems. - Mentor and provide technical leadership to AI/ML engineers.Qualifications & Experience- 7-10 years of experience in AI/ML, with strong hands-on exposure to LLMs and agentic or autonomous systems in the last 2-3 years. - Proficiency in Python and modern agent frameworks (e.g., LangChain, AutoGen, CrewAI, or similar). - Experience deploying AI systems in production on cloud platforms (AWS, GCP, or Azure). - Strong system design, problem-solving, and cross-functional leadership skills. - Knowledge of enterprise support models such as multilingual/24x7 helpdesk, tiered escalation structures, and SLA-driven service delivery. - Understanding of telecom-grade and SME-grade regulatory and compliance standards (e.g., GDPR, ISO 27001, SOC2, PCI, data residency controls). - Experience defining data access governance for AI agents including identity controls, audit logging, RBAC, and encryption standards. - Knowledge of secure AI agent development practices—data minimization, redaction, PII handling, and prompt governance.

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