Role Overview

As an AI Solution Architect specializing in Azure and AWS, you will lead the design, development, and production deployment of large-scale AI/ML solutions tailored for the insurance industry. You will work closely with cross-functional teams including data scientists, engineers, actuaries, and business leaders to transform business strategy into secure, scalable, and cost-effective AI architectures.

Key Responsibilities

AI Strategy & Use Case Development

Identify and prioritize AI/ML use cases across the insurance value chain: Underwriting, pricing, claims fraud detection, customer segmentation, policy recommendation engines, and chatbots.

Partner with business stakeholders (e.g., actuaries, underwriters, claims analysts) to define impactful AI-driven solutions that enhance decision-making and operational efficiency.

Architecture & Design

Design resilient, scalable, and cloud-agnostic AI/ML architectures using Azure and AWS.

Build and manage data ingestion and transformation pipelines using Azure Data Factory and AWS Glue.

Define and implement MLOps workflows using Azure ML Pipelines, AWS SageMaker Pipelines, and MLflow.

Technical Leadership

Lead design reviews, technical workshops, and blueprint sessions.

Mentor engineers and data scientists in best practices for model development, deployment, and cloud-native AI.

Solution Development

Implement NLP, computer vision, and deep learning solutions using Azure Cognitive Services, AWS Comprehend, Rekognition, and Bedrock.

Develop microservices/APIs (Python, FastAPI) for real-time inference and batch scoring.

Work with frameworks like TensorFlow, PyTorch, and Scikit-learn.

Integration with Insurance Systems

Ensure seamless integration with core insurance platforms: Policy Administration, Claims Management, Billing, CRM (e.g., Guidewire, Duck Creek, Salesforce).

Collaborate with enterprise architects to align AI with broader IT modernization initiatives.

Deployment & Operations

Containerize models using Docker, deploy via Kubernetes (AKS/EKS).

Implement CI/CD automation (Azure DevOps, AWS CodePipeline) and observability (CloudWatch, Prometheus, Azure Monitor).

Governance & Security

Enforce cloud security and data compliance using IAM, VNet, KMS, and encryption protocols.

Leverage Azure Responsible AI and AWS SageMaker Clarify for explainability, fairness, and auditability.

Stakeholder Engagement

Present technical architectures and value propositions to C-level executives, claims directors, and underwriting heads.

Serve as the bridge between business needs and AI/ML capabilities.

Required Qualifications

Experience:

8 10 years in AI/ML and software/system architecture.

5+ years in solution/technical leadership roles.

Education:

Bachelor s in Computer Science, Data Science, or Engineering.

Master s or PhD in AI/ML preferred.

Cloud Expertise:

Azure: Azure ML, Cognitive Services, Data Factory, Databricks, Cosmos DB

AWS: SageMaker, Comprehend, Rekognition, Glue, Redshift, DynamoDB

Tools & Frameworks:

Languages: Python (mandatory), Java or C++

ML Frameworks: TensorFlow, PyTorch, Scikit-learn

Big Data & Streaming: Spark, Kafka, Hadoop

MLOps/DevOps: Kubernetes (AKS/EKS), Docker, MLflow, Kubeflow, CI/CD pipelines

Preferred Qualifications

Certifications: Azure AI Engineer Associate, AWS Certified Machine Learning Specialty

Advanced AI Expertise: Generative AI (Azure OpenAI, ChatGPT, AWS Bedrock), Prompt Engineering, Agentic AI

Community & Research: Contributions to open-source projects or AI/ML publications

Soft Skills: Strong communication, stakeholder management, and strategic thinking. Team leadership and mentoring