C2C Role - AI Architect– Insurance (Mandatory) | Azure | API-First Microservices (.NET Program) - REMOTE - Only GC/USC
Rakesh Sharma
Dear Vendors
Please share resume for below role
Job Title: AI Architect– Insurance (Mandatory) | Azure | API-First Microservices (.NET Program)
Location: Remote/EST Candidates
Visa: Only GC/USC
Experience: 15+ years overall; 4+ years in AI/ML architecture/engineering
Role Summary
We are building a next-generation insurance platform, including a greenfield P&C Policy Administration System (PAS) with a microservices-based, API-first architecture on Microsoft .NET.
As the AI / ML Architect, you will lead the design and delivery of AI-powered capabilities across underwriting, pricing, claims, fraud, and operations. You will define end-to-end AI architecture (data → model → MLOps → serving), ensure secure and compliant AI, and partner closely with product, actuarial, underwriting SMEs, and engineering teams to move from prototypes to production-scale AI.
Insurance domain experience is mandatory for this role.
Key Responsibilities
1) AI Architecture & Solution Design (End-to-End)
- Define the target-state AI/ML architecture for insurance use cases: underwriting decision support, risk scoring, claims triage, fraud detection, pricing optimization, customer/agent assist, and personalization.
- Select and guide model approaches: predictive ML, LLMs/GenAI, NLP (and vision models where applicable), with clear tradeoffs and success metrics.
- Design API-first AI services that integrate cleanly with microservices (REST/gRPC, event-driven triggers, idempotency, versioning).
- Define patterns for feature pipelines, model serving, and governance that work across multiple pods and environments.
2) Model Engineering, MLOps & Deployment (Production Focus)
- Lead model development lifecycle: training, evaluation, validation, release, monitoring, and periodic refresh.
- Implement MLOps pipelines: automated model testing, monitoring, drift detection, model registries, approval workflows, and rollback strategies.
- Define serving patterns (batch/real-time/streaming) and optimize for accuracy, latency, reliability, and cost.
3) Insurance Domain Alignment (Business + Actuarial + Underwriting)
- Partner with product owners and translate requirements into AI-enabled components and measurable outcomes.
- Ensure AI outputs comply with underwriting guidelines, rating practices, claims workflows, and internal governance.
- Design human-in-the-loop controls where needed for regulated decisioning and operational safety.
4) Responsible AI, Security, Compliance & Risk
- Establish responsible AI guardrails: explainability, fairness/bias mitigation, audit trails, traceability, and model documentation standards.
- Ensure data privacy/security controls across the pipeline: PII handling, access controls, encryption, secrets management, and environment separation.
- Collaborate with risk/compliance to meet insurance regulatory expectations for AI systems (governance, reproducibility, reviewability).
5) Platform Integration & Cross-Functional Leadership
- Work closely with the Chief Architect, .NET architects, data architect, DevOps, and engineering pods to align AI services to platform standards.
- Mentor data scientists/ML engineers; enforce engineering rigor (testing, reliability, monitoring, secure coding).
- Drive POCs and technology evaluations, and productize successful capabilities into reusable platform services.
6) AI-Assisted Engineering Enablement (Claude Code, Cursor, MCP)
- Use Claude Code and Cursor as first-class development accelerators (code generation, refactoring, test generation, documentation), with strong review and security guardrails.
- Standardize patterns for tool usage across teams, including MCP-based workflows/integrations (where applicable), ensuring traceability and quality gates.
- Define measurement for productivity and quality improvements (cycle time, rework, defect leakage, release stability).
Must-Have Qualifications
Insurance Domain (Mandatory)
- Proven insurance industry experience is required (P&C preferred): underwriting, rating/pricing, claims triage, fraud, policy servicing, or insurance data/analytics.
- Experience designing or integrating ML/AI solutions in insurance decisioning contexts (e.g., risk scoring, pricing, fraud, claims).
Technical (Azure-first)
- 4+ years hands-on AI/ML engineering and/or architecture experience; overall experience typically 8–12+ years.
- Strong experience with Azure AI ecosystem, including one or more of:
- Azure Machine Learning (training, registries, endpoints)
- Azure OpenAI / LLM integration patterns
- Azure AI Services (language, vision, etc.)
- Strong MLOps experience: CI/CD for ML, model registries, monitoring, drift detection, evaluation, and controlled rollouts.
- Experience building API-first services and deploying ML systems using Docker and Kubernetes (AKS preferred).
Engineering & Collaboration
- Strong communication skills: can explain model tradeoffs and risks to non-technical stakeholders and client executives.
- Proven ability to lead cross-functional teams in fast-paced environments and ship production outcomes.
- Strong P&C insurance experience (Auto/Home/Commercial) and familiarity with PAS workflows.
- Experience with event streaming (Kafka/Event Hubs) and real-time inference/feature pipelines.
- Experience with responsible AI frameworks and interpretable ML methods in regulated environments.
- Azure certifications (Azure AI Engineer / Azure Solutions Architect).
Regards,
Rakesh Kumar
VedaSoft Inc.