Available to hire
Hi, I’m Lakshmi Pulicharla, an AI/ML Engineer and Data Scientist with 4 years of experience building scalable data and AI solutions across banking, healthcare, and e-commerce. I enjoy turning complex data into actionable insights using Python, R, and SQL, with hands-on experience in PyTorch, TensorFlow, scikit-learn, and Spark ML for predictive modeling, NLP, and risk analytics.
I love collaborating with product, analytics, and engineering teams to translate model outputs into practical decisions, ensure explainability and fairness, and deploy reliable ML systems in cloud environments like AWS and Azure. I also explore Generative AI and stay current with governance and regulatory best practices.
Language
English
Fluent
Work Experience
AI/ML Engineer at Central Bank
February 1, 2025 - PresentDesigned and deployed AI/ML solutions for banking use cases including transaction monitoring, fraud investigation, and regulatory reporting using Python-based ML and deep learning models. Built, trained, and deployed ML models on AWS SageMaker, supporting batch and near real-time risk scoring across large-scale transactional datasets. Engineered end-to-end feature pipelines using AWS Glue, transforming raw transactional, customer, and behavioral data into model-ready features for training and inference. Containerized ML and inference services with Docker; provisioned cloud infrastructure with Terraform; integrated SageMaker inference endpoints using AWS Lambda for near real-time decisioning. Implemented CI/CD pipelines for ML workflows and applied statistical analysis, model evaluation, and threshold tuning. Established monitoring and logging for model execution and data drift to ensure reliability in regulated environments. Leveraged Generative AI capabilities via OpenAI GPT-4 for aut
AI/ML Engineer at BJC Healthcare
May 1, 2024 - January 1, 2025Designed and developed machine learning and NLP models for healthcare use cases including patient risk stratification, readmission prediction, and clinical decision support. Built LSTM and CNN models to process structured data (vitals, lab results) and unstructured clinical notes. Developed AI-driven conversational and summarization solutions for EHR data using large language model frameworks, enabling clinicians to query patient records efficiently within secure environments. Prepared and curated healthcare datasets on Azure data platforms, performing data wrangling, normalization, and validation. Implemented NLP pipelines with spaCy and transformer-based models to extract medical concepts and insights from unstructured text. Applied dimensionality reduction and explainability techniques such as PCA and SHAP. Automated model training, evaluation, and deployment workflows using MLflow, with CI/CD on Azure Databricks. Orchestrated data processing and model retraining with Apache Airflow
ML Engineer at Wells Fargo
January 1, 2023 - November 1, 2023Developed and deployed ML models for banking use cases including credit risk assessment, fraud detection, and customer analytics using Logistic Regression, Random Forest, and Gradient Boosting. Productionized models by converting research notebooks into scalable Python pipelines. Implemented feature engineering via PySpark transforming transactional and customer data into model-ready features. Built end-to-end ML pipelines across data prep, training, validation, and batch inference on enterprise cloud platforms. Fine-tuned models with cross-validation and regularization. Built RESTful inference services using Flask and integrated with CI/CD pipelines. Implemented monitoring for model execution, data drift, and logging to support regulatory readiness.
Data Engineer at Myntra
August 1, 2021 - December 1, 2022Developed Python-based ETL/ELT pipelines for data ingestion, transformation, and validation, enabling ML-ready data delivery for personalization and analytics. Implemented and optimized SQL and PySpark pipelines for orders, inventory, pricing, and clickstream data. Built Spark-based data processing jobs for scalable processing of structured and semi-structured data. Integrated data sources into AWS S3 and Snowflake. Implemented data quality checks and reconciliation rules to ensure pipeline reliability. Developed near real-time ingestion using Spark and AWS Lambda. Created RESTful APIs with Flask to expose curated datasets and metrics for BI tools to analytics teams. Collaborated with data scientists, analysts, and product owners in Agile sprint cycles.
Education
Master’s degree in computer science at SouthEast Missouri State University
January 11, 2030 - May 1, 2026Qualifications
AWS Certified ML Associate
January 11, 2030 - May 1, 2026Industry Experience
Financial Services, Healthcare, Software & Internet
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