Principal - AI Architect - Insurance

About The Role

The applicant should have deep, hands-on experience architecting, designing, and implementing enterprise-grade AI, Machine Learning, and Generative AI solutions, and experience leading technical teams delivering complex AI and automation engagements across industries. Applicants should have some of the following experience:

• Experience architecting and implementing AI/ML solutions across multiple domains, including:
• Generative AI and Large Language Model (LLM) based solutions
• Predictive and prescriptive Machine Learning models
• Computer Vision
• Natural Language Processing (NLP) and Conversational AI
• Agentic AI systems and multi-agent orchestration.
• Worked across the end-to-end AI solution lifecycle: use case discovery, data assessment, solution architecture, model development, deployment, and monitoring.
• Experience translating business problems into AI solution architecture using design thinking and structured problem-solving techniques.
• Hands-on experience designing AI/ML platform architectures on cloud-native and hybrid environments (AWS, Azure, GCP).
• Hands-on experience with LLM frameworks and patterns (Lang Chain, Llama Index, Semantic Kernel), vector databases, and Retrieval-Augmented Generation (RAG) architectures.
• Experience with MLOps/LLMOps practices, including model lifecycle management, CI/CD for ML, monitoring, and retraining pipelines.
• Strong hands-on proficiency in Python and ML/AI frameworks such as Tensor Flow, PyTorch.
• Experience with cloud AI/ML services such as Azure OpenAI, AWS Bedrock/Sage Maker, and Google Cloud Vertex AI.
• Experience in data architecture and engineering to support AI initiatives, including data pipelines, feature stores, and data governance.
• Experience integrating AI solutions into enterprise systems and applications via APIs, microservices, and event-driven architectures.
• Experience with AI governance, responsible AI practices, model risk management, and data privacy/security considerations.
• Experience integrating AI/GenAI capabilities with automation platforms (e.g., UiPath, Power Automate) to enable intelligent automation.
• Travel to client sites and for practice work efforts is required on an as needed basis.

About The Role The applicant should have deep, hands-on experience architecting, designing, and implementing enterprise-grade AI, Machine Learning, and Generative AI solutions, and experience leading technical teams delivering complex AI and automation engagements across industries. Applicants should have some of the following experience:

• Experience architecting and implementing AI/ML solutions across multiple domains, including:
• Generative AI and Large Language Model (LLM) based solutions
• Predictive and prescriptive Machine Learning models
• Computer Vision
• Natural Language Processing (NLP) and Conversational AI
• Intelligent Document Processing (IDP / iOCR)
• Agentic AI systems and multi-agent orchestration.
• Worked across the end-to-end AI solution lifecycle: use case discovery, data assessment, solution architecture, model development, deployment, and monitoring.
• Experience translating business problems into AI solution architecture using design thinking and structured problem-solving techniques.
• Hands-on experience designing AI/ML platform architectures on cloud-native and hybrid environments (AWS, Azure, GCP).
• Hands-on experience with LLM frameworks and patterns (Lang Chain, Llama Index, Semantic Kernel), vector databases, and Retrieval-Augmented Generation (RAG) architectures.
• Experience with MLOps/LLMOps practices, including model lifecycle management, CI/CD for ML, monitoring, and retraining pipelines.
• Strong hands-on proficiency in Python and ML/AI frameworks such as Tensor Flow, PyTorch.
• Experience with cloud AI/ML services such as Azure OpenAI, AWS Bedrock/Sage Maker, and Google Cloud Vertex AI.
• Experience in data architecture and engineering to support AI initiatives, including data pipelines, feature stores, and data governance.
• Experience integrating AI solutions into enterprise systems and applications via APIs, microservices, and event-driven architectures.
• Experience with AI governance, responsible AI practices, model risk…