You will lead the engineering of production‑grade AI/GenAI services and agentic automations that uplift risk assessment, claims adjudication, and customer communications on a secure, multi‑tenant foundation.

Key Outcomes & Engineering Responsibilities

● Own a 12–24‑month Insurance AI engineering roadmap across underwriting (L&A;, P&C;,

Specialty), policy servicing, and claims—prioritized by business outcomes (STP, leakage reduction, TAT, loss ratio).

● Architect multi‑tenant AI services (LLM/RAG, risk/price models, adjudication engines) with

API‑first interfaces, solid tenancy isolation, observability, and cost/latency SLOs; enable consumption by Insurance WorkDesk/agent portals and core ecosystems.

● Underwriting intelligence: ship services for submission ingestion, triage/prioritization, risk scoring, quote‑acceptance prediction, and document summarization; integrate with rules engines and rating/policy admin systems.

● Claims AI: deliver FNOL intake automation, AI triage, fraud/risk detection, and explainable adjudication for Life, P&C;, and Health; standardize salvage/subrogation sub‑processes and omnichannel customer updates.

● Policy servicing & booking/binding: build GenAI‑assisted clause/wording libraries, template governance, and contract generation with maker‑checker workflows and full audit trails.

● Agentic insurance journeys: operationalize multi‑agent frameworks (e.g., Underwriting

Assistant, Claim Adjudication) with guardrails (input/output filters, grounding, policy catalogs) and human‑in‑the‑loop controls.

● Document intelligence (IDP): embed classification, extraction, and redaction for applications,

medicals, bills, loss evidence, and endorsements to reduce manual effort and errors.

● Customer communications:



expose AI services that personalize and govern omnichannel communications (renewals, endorsements, claim letters) with template control and auto‑archival.

● Ecosystem integrations: design adapters for core platforms (e.g., Guidewire, Duck Creek),

CRM, and data‑partner APIs; package deployables for marketplace motions where applicable.

● Establish insurance‑grade MLOps/LLMOps: model/data registries, offline/online evaluations

(grounding, fairness, leakage impact), CI/CD, blue‑green/canary rollouts, rollback, run‑books;

incident/SLA management.

● Build, coach, and scale a high‑performing team (applied science, ML/platform, evaluation &

safety); drive design rigor, reliability, and measurable production impact.

Requirements

● 10–12 years total; 5+ years leading AI/ML engineering teams shipping production AI in insurance (L&A;/P&C;/Specialty) across underwriting, policy servicing, or claims. ● Systems design depth: multi‑tenant AI services, vector/feature stores, streaming ETL, event architectures; observability and cost/performance optimization at scale.

● LLMs & decisioning: prompting, fine‑tuning, RAG; explainable decisioning aligned to underwriting/claims policies; propensity, fraud, and price‑sensitivity models.

● Document AI & IDP: OCR/ICR + layout models for medical records, bills, proofs; privacy/PII redaction; evidence packaging for audits.

● Domain fluency across Life & Annuity (policy issuance/underwriting, claims), P&C; (policy booking/binding, claims), and Health (auto‑adjudication, pre‑auth).

● Ecosystem experience with insurance cores/platforms (e.g., Guidewire, Duck Creek), CRM,

and data providers.

● Stakeholder leadership and communication; ability to explain model/platform trade‑offs to executives, regulators, and customers.

📌 AI Lead- Insurance (India)
🏢 MarketScope
📍 India