Lead Analyst – AI Ops Support

Lead Analyst – AI Ops Support

Department

None

Job Description

Job Description
Lead Analyst — AI Operations Support Team, Counterparty Credit Risk Technology
Lead Analyst – AI Ops Support
Experience
8–10 years Location
Chennai (preferred) / Pune Team size managed
3–4 analysts (Senior/Junior)
ROLE PURPOSE
Lead the offshore AI Ops Support function for the client's Counterparty Credit Risk (CCR) Technology team. The role is primarily an AI/ML operations role owing production monitoring, performance and active oversight, and automation of AI-enabled controls across CCR exposure, EPE/PFE and risk-reporting pipelines. A working knowledge of CCR concepts is a mandatory enabler, not the core of the job - required so the lead can correctly interpret alerts, detect false positives from genuine risk issues, and hold credible conversations with Risk, Model Risk and Technology stakeholders.
KEY RESPONSIBILITIES
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AI / ML Operations (core focus)
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Own end-to-end production monitoring of AI/ML-enabled operational processes across the CCR technology team.
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Track model performance, drift, data-quality degradation and output anomalies; define thresholds and alerting logic in partnership with Model Risk and Data Science.
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Establish human-in-the-loop review and escalation protocols for AI-flagged exceptions.
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Own the AI Ops tooling roadmap for the team: predictive monitoring, anomaly detection, automated prioritization and self-healing workflows.
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Act as the primary interface between AI Ops, Model Risk, Data Science and Engineering on all model-related production issues.
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Automation & Continuous Improvement
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Drive a team-wide automation backlog: automate manual monitoring, reconciliation and exception-management activities currently done manually.
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Build and maintain dashboards, automated alerts and AI-assisted investigation tools; measure and report reduction in manual touchpoints and operational risk.
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Prioritize and sequence automation initiatives against incident trends and MTTR data.
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Incident & Problem Management
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Primary escalation point for high-severity incidents across CCR applications and AI-enabled processes.
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Lead root-cause analysis for recurring calculation, data and model issues; own the permanent-fix backlog with Engineering.
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Track incident volumes, SLA breaches, MTTR and recurring-problem trends; report on operational health.
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CCR & Risk Context (supporting knowledge)
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Apply working knowledge of exposure, EPE/PFE, limits and collateral/margin concepts to correctly interpret monitoring alerts and data breaks.
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Coordinate investigation of exposure/data discrepancies with Risk and upstream/downstream technology teams, as an input to the AI Ops response, not the primary workstream.
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Governance, Team Leadership & Stakeholder Management
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Maintain SOPs, runbooks, escalation matrices and control evidence for audit and model-governance requirements.
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Manage work allocation, quality review, KPIs and cross-training across the analyst team.
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Provide regular operational health and improvement reporting to clients and other senior stakeholders.
REQUIRED SKILLS & EXPERIENCE
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8–10 years in technology operations / production support, with at least 3–4 years in an AI Ops, MLOps or AIOps-adjacent capacity.
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Demonstrated experience monitoring AI/ML models in production — performance, drift, anomaly detection, human-review workflows.
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Hands-on exposure to AI Ops/observability tooling (e.g. dashboards, alerting platforms, log/metric analytics; specific tool stack per client environment).
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Solid grounding in Counterparty Credit Risk concepts (EPE/PFE, exposure, limits, collateral) enough to independently prioritize whether an alert is a data, model or genuine risk issue.
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Prior team-lead or people-management experience in an offshore/onshore delivery model.
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Strong stakeholder communication — comfortable presenting operational updates to senior Risk/Technology/AD-level stakeholders.

Open Positions

None

Mandatory Skills

AIOps,MLOps,AI/ML Models,Observability,Counterparty Credit Risk,modeling

Education Qualification

Bachelor or Master’s degree in Computer science, Financial Engineering, Physics, Engineering or Mathematics

Experience

8 to 10 years