AI Solutions Architect
6 days ago
Details:
AI Solution Architect - Insurance Domain (Azure & AWS)
Location: Remote
Duration: 12 months
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.
- 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.
- Lead design reviews, technical workshops, and blueprint sessions.
- Mentor engineers and data scientists in best practices for model development, deployment, and cloud-native AI.
- 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.
- 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.
- Containerize models using Docker, deploy via Kubernetes (AKS/EKS).
- Implement CI/CD automation (Azure DevOps, AWS CodePipeline) and observability (CloudWatch, Prometheus, Azure Monitor).
- 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.
- 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.
Experience:
- 8-10 years in AI/ML and software/system architecture.
- 5+ years in solution/technical leadership roles.
- Bachelor's in Computer Science, Data Science, or Engineering.
- Master's or PhD in AI/ML preferred.
- Azure: Azure ML, Cognitive Services, Data Factory, Databricks, Cosmos DB
- AWS: SageMaker, Comprehend, Rekognition, Glue, Redshift, DynamoDB
- 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
- 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
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