AI Architect

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.

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 A I, 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

- Expertise in AI explainability tools (SHAP, LIME, attention visualization), bias detection frameworks (Fairlearn, AI Fairness 360), model risk management, and regulatory compliance frameworks. Deep understanding of AI ethics principles including fairness, accountability, transparency, and privacy-preserving machine learning techniques.

Required Experience 7+ years in software engineering,

data science , or

AI/ML

roles with demonstrable emphasis on the

Microsoft Azure ecosystem , including at least 03 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.

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.