Gen AI Architect-Insurance

Coforge; Dadri; On-site; INR 4,000,000 - 6,500,000; Full time

Job summary

Coforge is hiring for an Insurance AI Architect with 12-20 years of experience to design and govern AI/ML solutions for underwriting and claims workflows. The role emphasizes hands-on work with LLM/GenAI stacks, platform integration, and governance within an enterprise insurance environment.

The candidate should be proficient in modern AI platforms, MLOps practices, and platform decisions across Copilot Studio, Foundry, and custom AI stacks, ensuring alignment with client maturity and governance

Qualifications

• Extensive experience with LLMs, GenAI frameworks, and prompt engineering for underwriting/claims workflows.
• Strong knowledge of cloud-based AI platforms (Azure, AWS, GCP) and model selection criteria.
• Practical experience integrating AI in insurance data platforms (Duck Creek, Guidewire).

Skills

LangChain & GenAI

Azure OpenAI

MLOps & MLflow

Duck Creek / Guidewire

API & Microservices

Data Engineering

Job description

Experience: 12-20 Years

Work Mode: 5 Days from office

We at Coforge are hiring for Insurance AI Architect

AI/ML Technical Stack

• LLM & GenAI frameworks: LangChain, LlamaIndex, Semantic Kernel; prompt engineering, RAG architecture design, and agentic frameworks (CrewAI, or similar) for multi-step underwriting/claims workflows
• Foundation model platforms: Practical experience with Azure OpenAI, AWS Bedrock, Google Vertex AI, and Anthropic Claude/API; model selection criteria (cost, latency, accuracy) for insurance-specific tasks
• Traditional ML/predictive modeling: Regression, gradient boosting (XGBoost/LightGBM) for risk scoring and pricing models; familiarity with actuarial modeling concepts as they intersect with ML
• Document AI/IDP: OCR and intelligent document processing (Azure Form Recognizer, AWS Textract) for submission intake, ACORD forms, loss runs, and SOV (statement of values) processing a common Commercial/Specialty use case
• MLOps & deployment: Model versioning, monitoring, and governance tooling (MLflow, Azure ML, SageMaker); CI/CD for AI pipelines integrated with platform release cycles
• Microsoft AI Platform Expertise: Copilot Studio, Foundry and Microsoft ecosystem integration
• Platform decision-making: Judgment on when to use Copilot Studio (low-code, business-user-facing agents) vs. AI Foundry (custom, code-first, higher-control agentic solutions) vs. open-source/other cloud AI stacks, based on client maturity, governance needs, and use case complexity
• Cloud platforms: Azure, AWS, or GCP; landing zone design, data lake/lakehouse architecture for insurance data
• Data engineering: ETL/ELT pipelines pulling from Duck Creek/Guidewire data models into analytics or AI-ready stores; understanding of insurance data standards (ACORD, ISO)
• API & microservices design: RESTful API design, event streaming (Kafka), and how to expose AI capabilities as composable services consumable by both platforms

Insurance Industry & Platform Expertise

• Duck Creek Technologies: Working knowledge of Duck Creek OnDemand, Duck Creek Policy, Claims, Billing, and Rating modules; understanding of Duck Creek's Author/Manuscript configuration model, Duck Creek Data Insights, and Duck Creek's low-code/no-code extensibility framework
• Optional - Guidewire InsuranceSuite: Basic knowledge of PolicyCenter, ClaimCenter, and BillingCenter architecture, including Gosu scripting, PC/CC/BC data model, Integration Gateway, Cloud API, and Guidewire Cloud Platform (GWCP); familiarity with UI framework and Guidewire Marketplace apps
• Platform integration patterns: Experience with how AI services plug into both platforms via REST/SOAP APIs, event-driven architectures (Guidewire Cloud Events, Duck Creek's integration points), webhooks, and middleware (MuleSoft, Boomi, Azure Integration Services)
• Understanding of P&C Commercial & Specialty product structures: Rating algorithms, forms/endorsements, exposure bases, and how these map into platform data models

Governance & Compliance

• Working knowledge of model risk management, explainability techniques
• Familiarity with SOC 2, data privacy (state insurance data regulations), and responsible AI frameworks as applied to carrier environments