Looks like you have JavaScript disabled. For the full Twine experience, you will need to re-enable it.

I am a results-driven AI/ML Engineer with over nine years of progressive experience architecting and delivering enterprise-grade Machine Learning and Generative AI solutions across insurance, healthcare, telecom, banking, and SaaS domains. I have deep expertise in the full AI/ML lifecycle—from data ingestion and feature engineering through model training, deployment, and production monitoring—with a strong foundation in NLP, Large Language Models (LLMs), and Retrieval-Augmented Generation (RAG) architectures. I am proficient in building cloud-native, MLOps-enabled systems using Python, TensorFlow, PyTorch, LangChain, and Azure/AWS infrastructure. I enjoy translating complex business requirements into scalable intelligent solutions that drive operational efficiency, decision automation, and improved customer experiences. I have led cross-functional AI initiatives, built robust observability and governance practices, and delivered measurable impact across underwriting, claims, and compliance domains. My approach combines advanced prompting strategies, modular feature pipelines, and secure cloud architecture to deliver reliable GenAI workloads and multi-application AI services.…I am a results-driven AI/ML Engineer with over nine years of progressive experience architecting and delivering enterprise-grade Machine Learning and Generative AI solutions across insurance, healthcare, telecom, banking, and SaaS domains. I have deep expertise in the full AI/ML lifecycle—from data ingestion and feature engineering through model training, deployment, and production monitoring—with a strong foundation in NLP, Large Language Models (LLMs), and Retrieval-Augmented Generation (RAG) architectures. I am proficient in building cloud-native, MLOps-enabled systems using Python, TensorFlow, PyTorch, LangChain, and Azure/AWS infrastructure. I enjoy translating complex business requirements into scalable intelligent solutions that drive operational efficiency, decision automation, and improved customer experiences. I have led cross-functional AI initiatives, built robust observability and governance practices, and delivered measurable impact across underwriting, claims, and compliance domains. My approach combines advanced prompting strategies, modular feature pipelines, and secure cloud architecture to deliver reliable GenAI workloads and multi-application AI services.

Shaik Aasmeen

Data Scientist, AI Engineer, AI Developer, +2





I am a results-driven AI/ML Engineer with over nine years of progressive experience architecting and delivering enterprise-grade Machine Learning and Generative AI solutions across insurance, healthcare, telecom, banking, and SaaS domains. I have deep expertise in the full AI/ML lifecycle—from data ingestion and feature engineering through model training, deployment, and production monitoring—with a strong foundation in NLP, Large Language Models (LLMs), and Retrieval-Augmented Generation (RAG) architectures. I am proficient in building cloud-native, MLOps-enabled systems using Python, TensorFlow, PyTorch, LangChain, and Azure/AWS infrastructure. I enjoy translating complex business requirements into scalable intelligent solutions that drive operational efficiency, decision automation, and improved customer experiences. I have led cross-functional AI initiatives, built robust observability and governance practices, and delivered measurable impact across underwriting, claims, and compliance domains. My approach combines advanced prompting strategies, modular feature pipelines, and secure cloud architecture to deliver reliable GenAI workloads and multi-application AI services.…I am a results-driven AI/ML Engineer with over nine years of progressive experience architecting and delivering enterprise-grade Machine Learning and Generative AI solutions across insurance, healthcare, telecom, banking, and SaaS domains. I have deep expertise in the full AI/ML lifecycle—from data ingestion and feature engineering through model training, deployment, and production monitoring—with a strong foundation in NLP, Large Language Models (LLMs), and Retrieval-Augmented Generation (RAG) architectures. I am proficient in building cloud-native, MLOps-enabled systems using Python, TensorFlow, PyTorch, LangChain, and Azure/AWS infrastructure. I enjoy translating complex business requirements into scalable intelligent solutions that drive operational efficiency, decision automation, and improved customer experiences. I have led cross-functional AI initiatives, built robust observability and governance practices, and delivered measurable impact across underwriting, claims, and compliance domains. My approach combines advanced prompting strategies, modular feature pipelines, and secure cloud architecture to deliver reliable GenAI workloads and multi-application AI services.

Available to hire

I am a results-driven AI/ML Engineer with over nine years of progressive experience architecting and delivering enterprise-grade Machine Learning and Generative AI solutions across insurance, healthcare, telecom, banking, and SaaS domains. I have deep expertise in the full AI/ML lifecycle—from data ingestion and feature engineering through model training, deployment, and production monitoring—with a strong foundation in NLP, Large Language Models (LLMs), and Retrieval-Augmented Generation (RAG) architectures. I am proficient in building cloud-native, MLOps-enabled systems using Python, TensorFlow, PyTorch, LangChain, and Azure/AWS infrastructure. I enjoy translating complex business requirements into scalable intelligent solutions that drive operational efficiency, decision automation, and improved customer experiences.

I have led cross-functional AI initiatives, built robust observability and governance practices, and delivered measurable impact across underwriting, claims, and compliance domains. My approach combines advanced prompting strategies, modular feature pipelines, and secure cloud architecture to deliver reliable GenAI workloads and multi-application AI services.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Language

English

Fluent

Work Experience

AI/ML Engineer (Senior) at Westfield Insurance

August 1, 2025 - Present

Led end-to-end development of a Retrieval-Augmented Generation (RAG) platform using LangChain and GPT-4 to transform underwriting workflows into AI-driven decision support. Built enterprise GenAI applications on Azure OpenAI for intelligent claims summarisation and policy analysis. Implemented semantic search with Pinecone for context-aware retrieval across policy documents and regulatory filings. Deployed FastAPI microservices for LLM inference, engineered multimodal pipelines for structured/unstructured data, and established an MLOps framework with MLflow, Docker, and Kubernetes on Azure AKS. Led prompt engineering, data pipelines on Databricks, GPT-powered conversational AI integrations, and comprehensive model observability with Evidently AI and Prometheus. Designed secure cloud architecture with RBAC and API gateway patterns, and authored model governance documentation.

ML Engineer at Humana

March 1, 2024 - July 1, 2025

Designed production clinical NLP pipelines using Hugging Face Transformers to extract structured entities from EHR narratives. Built patient risk stratification models from longitudinal claims and clinical data and established end-to-end MLOps (MLflow, DVC) for reproducibility. Developed Spark/Databricks pipelines for large-scale healthcare data, deployed real-time inference endpoints via FastAPI, and implemented SHAP-based explainability. Implemented data quality checks with Great Expectations, automated ETL via Airflow, and centralized monitoring with Prometheus and Grafana. Built reusable clinical feature engineering pipelines and managed Azure cloud infrastructure for scalable deployments, ensuring HIPAA-compliant data handling and ongoing model retraining and validation.

Machine Learning Engineer at Verizon

July 1, 2021 - February 1, 2024

Developed a production-grade customer churn prediction system using XGBoost and ensemble techniques integrated into the CRM platform. Built customer segmentation models, engineered Spark/PySpark data pipelines on Databricks, and configured MLflow tracking for experiment management. Delivered real-time ML inference via FastAPI REST services and built batch-demand forecasting for network capacity and care volumes. Implemented automated CI/CD pipelines with Jenkins and containerized deployments, performed extensive feature engineering, and developed batch scoring for overnight analytics. Created model monitoring dashboards to track data drift and prediction quality in Power BI/Tableau.

Data Scientist (ML) at Deutsche Bank

November 1, 2019 - June 1, 2021

Developed credit risk scoring models (XGBoost, Logistic Regression) and time-series forecasting (ARIMA/Prophet). Built PySpark pipelines for ingesting large financial datasets, created ETL processes with AWS Glue/S3, and implemented SHAP explainability for model risk management. Used AWS SageMaker for scalable training and batch inference, developed SQL-based data transformations, and delivered Power BI dashboards to visualize risk metrics. Conducted EDA, hyperparameter tuning, and cross-domain data integration for robust risk analytics and validation.

Data Scientist – NLP at Zoho Corp

July 1, 2015 - August 1, 2019

Built NLP-based support ticket classification with Scikit-learn and SVM, developed intent detection models, and established standard NLP preprocessing pipelines (NLTK, spaCy). Implemented TF-IDF and bag-of-words features, contributed to a Named Entity Recognition system, and developed sentiment analysis for customer feedback. Created ARIMA-based time-series forecasts for ticket volumes and built Flask-based REST APIs for model serving. Performed data cleaning, feature engineering, and model evaluation, collaborating across Agile teams.