Job Purpose
We are seeking a seasoned leader to drive Agentic AI adoption across the B2C, EMI Card, and Payments verticals.
This senior role will lead domain strategy, solution design, and delivery of agentic AI use cases.
The role requires strong orchestration leadership and close coordination with the Agentic AI tech team to ensure high quality, domain grounded autonomous agent solutions
Duties and Responsibilities
Lead the end-to-end domain design and delivery of Agentic AI use cases across B2C, EMI Card, and Payments verticals.
Translate business goals into domain reasoning frameworks and orchestration logic.
Define and supervise thought logging, reasoning trace standards, and interpretability guidelines.
Collaborate with tech teams to architect modular reasoning systems and multi-agent workflows.
Oversee project management, ensuring timely delivery and alignment with business OKRs.
Build and maintain domain knowledge bases, taxonomies, rule engines, and planning structures.
Define orchestration rules, fallback strategies, escalation logic, and reliability guardrails.
Partner with cross-functional teams to ensure safe, compliant, domain-grounded agent behavior.
Drive continuous improvement via evaluation cycles, feedback loops, and refinements.
Identify and prioritize new Agentic AI opportunities across assigned verticals
Key Decisions / Dimensions
Selection of reasoning strategies, orchestration patterns, and collaboration models.
Determining logging granularity, cognitive checkpoints, and interpretability levels.
Approval of fallback behaviors and escalation rules.
Prioritization of vertical wise agentic AI initiatives.
Definition of domain specific evaluation and benchmarking criteria.
Major Challenges
Domain-Grounded Reasoning Design
Translating implicit expert reasoning into explicit, machine-executable decision frameworks.
Managing uncertainty, ambiguity, and contradictory signals in domain data without oversimplifying.
Interpretability, Logging, and Observability
Designing logging schemas that support post-hoc reasoning audits without creating overhead or data bloat.
Balancing performance with observability, especially in production settings.
Strategic Autonomy vs Control
Deciding when agents should self-direct vs. escalate decisions to humans.
Embedding guardrails that are both domain-relevant and adaptive under evolving agent capabilities.
Feedback Loop Complexity
Designing multi-layered feedback systems: self-reflection user feedback domain overrides model fine-tuning.
Managing feedback prioritization: what gets logged, analyzed, and appliedand what gets ignored
Required Qualifications and Experience
810 years of experience in AI / Agentic AI.
Understanding of Microsoft Co pilot and Agentforce ecosystem.
Experience with orchestration-heavy, domain-driven AI system design.
Knowledge of LLM architectures, agent planning frameworks, and reasoning traces.
Strong stakeholder communication and project leadership skills.
Sourced from naukri
