Available to hire
I am an AI/ML Engineer with 5 years of hands-on experience building scalable machine learning and NLP solutions for healthcare and financial services. I excel at turning data into actionable insights, automating MLOps pipelines, and deploying generative AI applications. I am passionate about delivering models that are accurate, interpretable, and aligned with regulatory standards, while driving faster decision-making and business impact.
In my work, I enjoy collaborating with cross-functional teams to translate complex requirements into robust AI solutions, continuously improving model performance, and lowering release cycles through automation and modern orchestration tools.
Skills
Work Experience
AI Engineer at Charles Schwab
July 1, 2023 - PresentLed design and training of PyTorch-based NLP models to improve sentiment analysis accuracy by 30% across financial products. Built end-to-end ML pipelines on AWS SageMaker and FastAPI to automate workflows and cut deployment cycles by 40%. Integrated AI insights into Power BI dashboards to accelerate executive decision-making and improve reporting speed by 35%. Built scalable data pipelines with Pandas and Spark, enabling faster preprocessing and model training. Collaborated with cross-functional teams to translate business requirements into scalable AI solutions for risk and compliance. Enhanced model interpretability using SHAP and LIME, and tuned hyperparameters to align with FINRA compliance standards. Engineered data ingestion with Kafka and Airflow, enabling real-time analytics and reducing latency by 50%. Established CI/CD pipelines with Git and Jenkins to automate builds, tests, and deployments.
ML Engineer at Synopsys Inc
January 1, 2022 - July 1, 2023Developed PyTorch-based NLP model boosting sentiment analysis accuracy by 30%. Designed, developed, and deployed predictive credit risk and loan default models using Regression, Decision Trees, Random Forests, Naive Bayes, and SVM. Built end-to-end MLOps pipeline leveraging MLflow, CI/CD for ML, and AWS services (Lambda, Glue, S3, Redshift), reducing model release cycles by 40%. Fine-tuned Hugging Face Transformers (BERT, GPT) and built custom LLM-based Q&A tools for internal audit teams, reducing manual review effort by 30%. Led generative AI use cases, including an internal chatbot for customer service and loan advisory with LangChain and GPT-based models. Automated risk reporting workflows and model monitoring dashboards using Python, SQL, AWS Athena, and Tableau, saving 80+ analyst hours per quarter and preventing SLA breaches by 10%.
Junior Machine Learning Engineer at Bytecraft System
March 1, 2019 - June 1, 2020Created data analysis and predictive modeling initiatives using Python (Scikit-learn) and Spark (MLlib, PySpark), developing advanced segmentation and analysis algorithms, which improved lifetime value prediction by 25%. Designed and deployed end-to-end regression models (Linear Regression, Random Forest, Decision Trees) using SQL for data extraction, Python for ETL processing, and Power BI for data visualization, enabling data-driven decision-making. Designed time-series forecasting models (ARIMA, Prophet, Holt-Winters) to predict monthly loan demand and branch-level cash flow requirements, improving forecast accuracy and mitigating cash shortages. Tuned hyperparameters and automated training pipelines for XGBoost models, shortening training time by 30% and improving model performance metrics significantly. Built and operationalized deep learning pipelines in TensorFlow for classification, regression, and sequence tasks, minimizing manual intervention and accelerating deployment cycle
Education
Master of Science in Computer Science at University of Central Missouri
August 1, 2020 - January 1, 2022Bachelor of Engineering Computer Science at Visvesvaraya Technological University (VTU)
August 1, 2015 - May 1, 2019Qualifications
Industry Experience
Healthcare, Financial Services
Skills
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