Available to hire
I’m a Machine Learning Engineer with 6+ years of experience building and deploying scalable ML, Deep Learning, MLOps, and AI solutions. I’ve led end-to-end ML pipelines and built predictive models, NLP applications, recommendation engines, and cloud-native platforms using Python, TensorFlow, PyTorch, AWS, Azure, Databricks, and Kubernetes.
I have hands-on experience with Generative AI, LLMs, RAG, AI agents, vector databases, and MLOps automation; I thrive on turning complex data into production-ready AI that drives business value.
Skills
Work Experience
Machine Learning Engineer at Morgan Stanley
June 1, 2024 - PresentDesigned and deployed enterprise ML and AI solutions for risk assessment, fraud detection, customer intelligence, and operational automation. Built scalable MLOps pipelines using Python, PySpark, Databricks, MLflow, and AWS SageMaker, supporting 15M+ monthly transactions and reducing deployment time by 60%. Developed fraud detection and customer risk scoring models with XGBoost and LightGBM, improving accuracy by 28%, reducing false positives by 22%, and boosting risk assessment accuracy by 25%. Implemented enterprise data engineering and real-time inference solutions using Databricks, Delta Lake, Snowflake, Kafka, and Spark Streaming, processing 5TB+ daily data and reducing prediction latency by 35%. Established ML Ops frameworks with Kubeflow, MLflow, Docker, Kubernetes, Jenkins, and GitHub Actions, enabling automated monitoring and drift detection and reducing production incidents by 30%. Built Generative AI apps using OpenAI GPT, LangChain, LangGraph, RAG, Pinecone, FAISS, and AWS
Graduate Research Assistant – Machine Learning at University of Memphis
May 1, 2023 - May 31, 2024Mentored 100+ students in Python, Data Science, and Machine Learning, guiding data preprocessing, feature engineering, model development, and predictive analytics techniques. Developed, trained, and evaluated ML models using Python, Pandas, NumPy, and Scikit-Learn; performed exploratory data analysis (EDA) and created interactive visualizations using Tableau, Matplotlib, and Seaborn. Collaborated with faculty and research teams on data-driven projects, conducting statistical analysis, model validation, and insight generation to support academic research initiatives.
Machine Learning Engineer at Infosys
August 1, 2019 - December 31, 2022Developed predictive analytics, underwriting intelligence, claims prediction, and ML solutions for insurance operations. Built cloud-based ML platforms processing large-scale data and predictive modeling initiatives. Implemented predictive ML models for policy renewal, customer retention, and claims forecasting using XGBoost, Random Forest, and Gradient Boosting, improving retention rates by 18% and prediction accuracy by 24%. Designed and optimized ETL pipelines using Python, PySpark, Spark, and Azure Databricks, processing over 10M policy records. Built NLP-based document classification systems and underwriting risk assessment solutions, reducing manual review by 35% and streamlining claims processing. Trained and deployed deep learning models using TensorFlow and Keras for customer behavior analytics, improving model performance by 20%. Implemented end-to-end MLOps workflows with MLflow, Apache Airflow, Azure ML, and Docker; automated model retraining and reduced manual operational
Education
Master of Science in Computer Science at University of Memphis, Memphis, TN
January 1, 2023 - December 31, 2024Qualifications
Industry Experience
Financial Services, Software & Internet, Professional Services
Skills
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