AI Architect

AI ARCHITECT

Our Purpose and Mission

Role Summary

Work with us to build modern Insurtech AI underpinned solutions, we are a growing team of hands on architects striving to build high quality solutions for our internal and external customers. The AI Architect is responsible for designing and implementing artificial intelligence and machine learning solutions across the ecosystem. This role combines deep expertise in AI/ML technologies with solution architecture to deliver intelligent, scalable, and responsible AI systems that drive business value.

Key Responsibilities

The AI Architect designs and implements enterprise-scale artificial intelligence and machine learning solutions across the insurance ecosystem, leveraging the Microsoft Azure AI platform as the primary technology stack while maintaining flexibility to incorporate Google AI capabilities where strategically advantageous.

This role combines deep technical expertise in AI/ML with solution architecture to deliver intelligent, scalable, and responsible AI systems that drive measurable business value.

Working extensively with existing AI teams, including data scientists, the architect brings architectural rigor and best practices to enhance and scale current AI capabilities.

The role focuses on architecting hybrid AI solutions that combine public large language models (GPT-4, Claude, Gemini) for general-purpose reasoning with custom-trained small language models fine-tuned on proprietary insurance data for domain-specific business processes including underwriting automation, claims processing, policy administration, and fraud detection.

The architect helps define the AI strategy and roadmap aligned with insurance industry business objectives, evaluating and selecting appropriate frameworks, platforms, and tools.

Required Skills

Microsoft AI Technology Stack

Deep expertise in Azure OpenAI Service (GPT-4, embeddings, document processing), Azure Machine Learning (Automated ML, MLOps, model registry, managed endpoints), Azure AI Services (Document Intelligence, Cognitive Search), Azure AI Studio (prompt flow, evaluation tools), Azure Databricks (unified analytics, MLflow integration), Azure Synapse Analytics, and Microsoft Fabric for integrated data science workloads.

Multi-Platform AI Capabilities

Experience with Google Cloud AI Platform including Vertex AI, Gemini multi-modal capabilities, and BigQuery ML for in-database machine learning on large insurance datasets, balanced with primary Microsoft stack expertise.

AI/ML Frameworks & Model Development

Proficiency in working with large language models (GPT-4, Claude, Gemini, LLaMA) alongside training and fine-tuning small domain-specific models using BERT, DistilBERT, and RoBERTa. Strong command of PyTorch, TensorFlow, scikit-learn, XGBoost, and the Hugging Face ecosystem including Transformers, PEFT for parameter-efficient fine-tuning, and model compression techniques including quantization, pruning, and knowledge distillation.

Orchestration & Automation

Hands-on experience with n8n workflow orchestration for AI pipeline automation and integration with Azure services, complemented by knowledge of Azure Logic Apps, Power Automate, MLflow for experiment tracking, and Azure DevOps or GitHub Actions for CI/CD pipelines supporting ML operations.

Insurance Domain AI Applications

Practical understanding of AI applications across the insurance value chain including underwriting automation (risk assessment, pricing optimization), claims processing (triage, fraud detection, damage assessment), document processing (OCR, contract analysis, regulatory document understanding), customer service automation, actuarial analytics (loss prediction, reserves estimation), and regulatory compliance automation.

Responsible AI & Governance

Required Experience

Seven or more years in software engineering, data science, or AI/ML roles with demonstrable emphasis on the Microsoft Azure ecosystem, including at least three years architecting and deploying production AI/ML systems at scale, preferably within insurance or financial services environments. Proven track record of training and fine-tuning small language models for domain-specific business processes, with strong collaborative experience working alongside AI/ML teams, data scientists, and ML engineers.

Hands-on experience implementing n8n workflow orchestration for AI pipelines, establishing AI governance frameworks in regulated industries, and working with both public LLMs and private custom model deployments. Demonstrated success building insurance-specific AI solutions and designing hybrid architectures that combine large and small models for cost-effective, high-performance systems. Evidence of improving existing AI systems and processes through architectural innovation and best practices.

Key Competencies

Insurance Domain Knowledge - Understanding of insurance business processes, data structures, regulatory requirements, actuarial concepts, and risk modeling principles specific to the insurance industry.

Technical Leadership & Collaboration - Demonstrated ability to work effectively with existing AI experts and teams, mentor data scientists and ML engineers on architectural best practices, lead technical discussions and design reviews, and bridge the gap between AI research and production implementation.

Innovation & Continuous Improvement - Commitment to staying current with AI/ML research and industry trends, evaluating emerging technologies for organizational adoption, driving proof-of-concepts for innovative insurance AI applications, and fostering a culture of experimentation and continuous learning.

Certifications

Microsoft Certified: Azure AI Engineer Associate (AI-102), Microsoft Certified: Azure Data Scientist Associate (DP-100), and Microsoft Certified: Azure Solutions Architect Expert (AZ-305). Additional valuable certifications include Google Professional Machine Learning Engineer.