AI Architect– Insurance (Mandatory) | Azure | API-First Microservices (.NET Program)

Role: AI Architect Insurance (Mandatory) Azure API-First Microservices (.NET Program)

Duration: Long Term

Location: Remote/ EST 

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 .

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 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).

Additional Information :

All your information will be kept confidential according to EEO guidelines.

Remote Work :

Yes

Employment Type :

Contract