Function: Insurance (Operations, Products & Technology)

Experience: 8 - 12 years (with 4+ years in applied AI/ML and 2+ years leading delivery)

Location: Gurgaon (WFO)

Role summary

We are hiring an AI/ML Lead to own how AI is applied across insurance

workflowsunderwriting support, claims, policy servicing, document-heavy operations, and customer/agent experiences. You will sit between business and technology: translate operational problems into AI solutions, set delivery standards, and ensure models and platforms actually move cycle time, accuracy, leakage, and customer experiencenot just PoCs.

You will lead a small AI/ML squad, partner with product, operations, risk/compliance, and engineering, and take solutions from problem framing through production and adoption.

What you will do

Business partnership

• Work with claims, underwriting, operations, and product leaders to identify high-value AI use cases (document intake, extraction, decision support, fraud/leakage signals, servicing automation, knowledge assistants).

• Convert messy insurance processes into transparent problem statements, success metrics,and phased roadmaps.

• Set expectations with stakeholders on accuracy, exceptions, human-in-the-loop, and what AI will not do.

• Communicate progress, risk, and ROI in business language; bring technologyconstraints back to the business early.

Solution ownership (not model-shopping)

• Design end-to-end AI solutions: data processing intelligence APIs/workflows ops feedback loop.

• Choose the right approach for the problem (classical ML, NLP, computer vision, GenAI, hybrid) without locking the org to a single technique.

• Own quality: evaluation design, error analysis, sampling, thresholding, and continuous improvement from production feedback.

• Ensure solutions are production-grade: latency, cost, reliability, fallbacks, auditability, and PII/sensitive-data handling.

Insurance domain depth

• Understand policy, claims, FNOL, endorsements, KYC/onboarding, correspondence, and operational SLAs well enough to challenge requirements.





• Design for insurance document reality: multi-page PDFs, scans, mixed quality, structured + unstructured content, exceptions, and regulatory language.

• Align AI outputs to downstream systems (core, claims, CRM, workflow) and to underwriting/claims decisioning—not standalone demos.

Leadership & operating model

• Lead engineers and data scientists: hiring bar, coaching, code/design reviews, delivery cadence.

• Coordinate with platform, data, security, and application teams on architecture, environments, and release.

• Define the AI delivery playbook: discovery, MVP, evaluation, UAT with ops, go-live, monitoring, and model/process change control.

• Build trust with risk, legal, and compliance on explainability, documentation, and responsible use of AI in regulated workflows.

• Manage vendors and internal build-vs-buy where it affects speed, cost, or control.

What we look for Leadership

• Proven ability to lead AI/ML delivery with mixed seniority; you have been the person business and engineering both trust.

• Comfortable in ambiguity: you frame the problem, sequence the work, and protect the team from thrash.

• Strong facilitation: workshops with ops SMEs, alignment with tech leads, executive- ready updates.

• Ownership mindset: you stay with a solution after launch until it is used and stable. AI/ML craft (applied, not academic)

• Hands-on history shipping AI/ML into production (not only notebooks or vendor configuration).

• Broad applied range: supervised ML, NLP/document intelligence, vision where relevant, and GenAI systems (including retrieval, grounding, and structured outputs).

• Judgment on when GenAI is the right tool vs.



when simpler ML or rules + ML is safer and cheaper.

• Experience with evaluation, drift, cost/latency trade-offs, and production failure modes.

• Enough engineering fluency to partner on APIs, data pipelines, cloud, and integration—without needing to be the deepest specialist in every library.

Insurance / regulated operations

• Direct experience applying AI/ML in insurance (P&C;, health, life, or related—claims, UW, servicing, or distribution).

• Familiarity with operational KPIs: TAT, straight-through processing, leakage, first- pass accuracy, exception rates, CSAT/NPS.

• Respect for audit, consent, data minimization, and model risk in a regulated environment.

Working style

• Python-centric AI delivery background; comfortable with modern cloud and production services.

• Can write a crisp PRD-style problem brief and a technical design that engineers can execute.

• Bias to measurable outcomes over tool lists.

Nice to have

• Built or scaled an AI CoE / chapter in a carrier, TPA, broker, or insurtech.

• Experience with document-heavy operations (policy packs, medicals, invoices, IDs, correspondence).

• Exposure to MLOps/governance (experiment tracking, versioning, CI for models, access control).

• Prior coordination of offshore/onshore or vendor + in-house hybrid teams.

Success in 12 months

• A prioritized insurance AI roadmap agreed with business and tech, with 2–3 production use cases live and measured.

• Clear operating rhythm: intake, evaluation, production SLAs, and a feedback loop with operations.

• A team that can deliver without heroics; stakeholders who can explain what the AI does, when it fails, and who owns the exception.

How this differs from an individual-contributor engineer role

This is a lead seat. Stack choices (frameworks, serving, OCR engines, vector stores, etc.) are means, not the job.

The job is: pick the right insurance problems, align business and technology, ship reliable AI into workflows, and grow the team that keeps improving them.

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