Job Summary

Architect role in a multinational organization focusing on design and implementation of scalable machine learning and artificial intelligence platforms for life and annuities insurance. Position requires extensive hands on work in ML Ops MLFlow Jenkins and AWS machine learning services to enable secure and reliable model delivery in a hybrid work model with day shifts.

Responsibilities

- Design robust and reusable machine learning architectures that support life and annuities insurance products and analytical solutions for global business teams in a scalable manner.
- Develop end to end ML Ops pipelines that cover data ingestion feature engineering model training validation and deployment using MLFlow Jenkins and AWS native capabilities.
- Implement model versioning model registry and experiment tracking using MLFlow to ensure reproducible experiments and auditable model lifecycle for regulatory compliant insurance environments.
- Build and optimize continuous integration and continuous delivery workflows using Jenkins and AWS Code Pipeline to automate testing packaging and deployment of artificial intelligence solutions.
- Coordinate closely with actuarial and underwriting stakeholders to translate insurance concepts into machine learning features and generative AI use cases that improve risk assessment and policy servicing.
- Define and enforce best practices for code quality configuration management observability and operational monitoring across all AI and ML solutions within the assigned portfolios.




- Create reference architectures and templates for reusable components such as data transformation utilities feature stores and model serving layers tailored to life and annuities insurance data structures.
- Guide teams in applying foundational AI and ML concepts to real world insurance problems including lapse prediction claims propensity fraud detection and customer engagement journeys.
- Evaluate select and integrate AWS machine learning services such as managed training model hosting and workflow orchestration to reduce operational overhead and improve reliability.
- Establish rigorous performance fairness and stability evaluation standards for machine learning and generative AI models to ensure ethical and compliant outcomes in customer facing insurance applications.
- Prepare detailed technical documentation solution diagrams and operational runbooks that enable smooth handover support and future enhancements by delivery and operations teams.
- Collaborate with security compliance and data governance partners to ensure that all ML Ops practices align with enterprise policies privacy expectations and regional insurance regulations.
- Mentor junior practitioners in AI and ML concepts ML Ops practices and insurance domain knowledge to build a sustainable talent pipeline for the organization.

Qualifications





- Require extensive practical experience in ML Ops including MLFlow Jenkins AWS Code Pipeline and associated automation tools for at least nine years in production contexts.
- Require robust proficiency in core AI and ML concepts covering supervised learning unsupervised learning model evaluation metrics and model drift management in enterprise environments.
- Require demonstrable hands on work with AWS machine learning services including managed training model hosting and pipeline orchestration components for large scale solutions.
- Require proven background in life and annuities insurance covering policy administration product structures pricing constructs and claim processes to align technical design with domain needs.
- Require strong experience in machine learning and artificial intelligence solution delivery including data preparation feature design model selection and deployment for business critical use cases.
- Nice to have experience with generative AI frameworks and large language model integration for use cases such as document processing customer communication and knowledge retrieval in insurance.
- Nice to have exposure to hybrid work environments and distributed teams with ability to coordinate across locations while maintaining consistent delivery standards and communication practices.
- Nice to have familiarity with broader cloud native architectures containerization and infrastructure as code to support resilient and maintainable AI and ML platforms.

Certifications Required Preferred certifications AWS Certified Machine Learning Specialty or equivalent ML Ops certification relevant to cloud based artificial intelligence platforms

📌 Architect (Chennai)
🏢 Cognizant
📍 Chennai