Driving GenAI-Powered Transformation in Insurance & Regulatory Tech

I lead the design and deployment of GenAI and RAG microservices that transform underwriting, claims, and compliance workflows for insurers, delivering measurable gains in productivity, accuracy, and turnaround.

🔹 Underwriting & Legal Automation

Led the build of a Python-based, microservice Retrieval-Augmented Generation (RAG) platform, accelerating contract renewal reviews by 50%. Introduced a LangGraph-based ingestion pipeline using Pydantic, Docker, and GitHub Actions, cutting manual data errors by 20%. Streamlined legal wording reviews from 5 to 2 days through GenAI-powered workflow optimization.

🔹 Claims Handler AI Copilot

Pioneered an agentic RAG assistant using GPT-4o, LangGraph agents, and vector search—boosting document review efficiency by over 50%. Delivered AI-generated evidence summaries and next-best-actions via custom entity extraction, saving up to 63% investigation time.

🔹 Regulatory Compliance AI

Engineered GenAI microservices to automate extraction and validation of FCA/PRA obligations, enhancing compliance team efficiency by 40%. Aligned AI-driven rules with PRA/FCA Handbook using RAG and GPT-4o, and enabled real-time GRC monitoring with Kafka integrations.

🔹 Policy Summarisation Platform

Delivered a greenfield solution using Vertex AI, Cloud Vector Search, and hybrid retrieval to reduce 100+ page insurance policies to digestible 1-page briefs. Achieved a 0.88 F1 score against SME gold standards using Deep Eval.

🔹 Indemnity Evidence Pipeline

Built a GenAI + Pydantic NLP system to extract key facts from claimant documents—reducing investigation time per claim by 50% and improving triage speed via custom entity recognition.

Statistical Modelling | Predictive Analytics | ML Model Integration

Led end-to-end development of advanced statistical models and analytics tools to extract insights, segment customers, and operationalize data science solutions across cloud environments.

🔹 Customer Segmentation & Sentiment Analysis

• Developed K-Means clustering models to segment users by income, spending patterns, and credit behavior

• Built and deployed a sentiment analysis system to classify and score hotel reviews, enhancing customer experience analytics

🔹 Predictive Modeling & Validation

• Designed, validated, and deployed regression-based analytical tools including decision trees, random forests, and ensemble models

• Applied bagging and boosting techniques to optimize model performance and ensure robustness across diverse datasets

🔹 Dimensionality Reduction & Bayesian Modeling

• Used Principal Component Analysis (PCA) to address multicollinearity and simplify complex datasets

• Integrated Bayesian Model Averaging (BMA) to combine and calibrate keyword-level prediction models, improving forecasting accuracy

⚙️ DevOps & DataOps Enablement | CI/CD Automation | Cloud Optimization

Drove enterprise-wide DevOps transformation by leading automation, cloud integration, and CI/CD pipeline initiatives—accelerating deployment cycles, reducing manual overhead, and cutting infrastructure costs.

🔹 DataOps & Cloud Integration

• Designed and managed data pipelines to support ML microservices across AWS and GCP

• Deployed scalable, containerized model delivery workflows with automated testing, enabling continuous delivery for data science teams

🔹 DevOps Tooling & Automation

• Oversaw end-to-end setup, Dockerization, and maintenance of core DevOps tools across the organization

• Automated provisioning of TeamCity servers, build agents, and Apache services using Ansible, reducing manual tasks by 90%

• Upgraded TeamCity from v9.1.7 to v2019.2.1 seamlessly, boosting CI/CD performance by 65%

🔹 Application Development

• Delivered zero-defect features for the in-house Enterprise Deployment Tool (EDT), enhancing deployment reliability

• Developed a single-click onboarding solution to automate DevOps setup for new teams, improving time-to-readiness

🔹 Cloud Enablement & Cost Optimization

• Led secure adoption of public cloud infrastructure, cutting infrastructure spend by 45% through efficient provisioning and usage strategies

🤖 ML Engineering | MLOps | DevOps Automation | Cloud-Native Architecture

Delivered scalable machine learning solutions and automated DevOps workflows across cloud infrastructure, accelerating model lifecycle, reducing costs, and enhancing security and developer onboarding.

🔹 Machine Learning Operations (MLOps)

• Built and maintained end-to-end pipelines for training, validation, and deployment of ML models

• Implemented production-grade monitoring to track model performance, drift, and degradation over time

🔹 Predictive Modeling & Analytics

• Developed a telecom churn prediction engine using KNN and logistic regression for customer retention initiatives

• Directed full-cycle development and evaluation of predictive models including decision trees, random forests, and ensemble techniques (bagging & boosting)

🔹 DevOps Automation & Cloud Architecture

• Architected CI/CD pipelines for Java applications and hosted core DevOps tooling in AWS, streamlining onboarding and release workflows

• Re-architected Java apps using AWS Lambda, API Gateway, and RDS—achieving 30% infrastructure cost savings

• Enhanced application availability and security through WAF, GuardDuty, AWS Config, ELB, auto-scaling, and CloudWatch

🔹 Key Achievements

• Built a web app to auto-generate AWS CloudFormation templates and Ansible playbooks, reducing manual setup time by 80%

• Integrated ServiceNow and Jira into in-house solutions, eliminating third-party integration costs and saving $1,140 in licensing fees

💻 Full-Stack Java Development | Application Modernization | DevOps Integration

Contributed to the development and optimization of enterprise applications using Java, Spring, Struts, and Hibernate, enhancing system performance and automating core business processes.

🔹 Application Development & Support

• Revamped the IRIS enterprise application using Spring, Struts, and Hibernate, resulting in a 60% improvement in performance

• Designed and implemented DevOps pipelines to automate Java web app deployments, improving release efficiency by 40%

🔹 Enterprise Automation & Process Optimization

• Developed a highly efficient ERP application to manage employee lifecycle processes including payroll, hiring, and resource management

• Engineered automation in Documentum-based research management tools, reducing manual document handling by over 90%

🔹 Key Technologies

Java · Spring MVC · Hibernate · Struts · DevOps · Documentum