AI Architect-Insurance (Mandatory)

About US

At Texnere, we are committed to helping businesses find the right talent, at the right time, through the right approach. Our 360° talent solutions seamlessly integrate flexible hiring models—Flexible Staffing, Team Leasing, Synergizing Agenting AI, and Managed Services—with deep industry expertise across IT, BPM, Sales & Marketing, and more. We understand that every organization is unique, operating within specific sectors and organizational ‘vectors’ that present distinct challenges and opportunities. Whether you''''''''re a healthcare startup looking to innovate or a global captive center (GCC) in the tech space, Texnere provides specialized talent solutions tailored to your precise needs.

Job title : AI Architect-Insurance domain

Location : Remote

Ezperience : Minimum 15 years

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 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).