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I am an AI/ML Engineer and Data Scientist with 4 years of experience designing, developing, and deploying scalable data, machine learning, and AI solutions that transform large-scale data into actionable insights across banking, healthcare, and e-commerce. I am proficient in Python, R, and SQL, with hands-on experience using PyTorch, TensorFlow, scikit-learn, and Spark ML for predictive modeling, NLP, and risk analytics. I build robust data extraction, preprocessing, and feature engineering pipelines with PySpark, Pandas, and NumPy to prepare high-quality datasets for training, validation, and inference. I have hands-on experience deploying and operating ML models on AWS SageMaker, Azure Databricks, and containerized inference services, with a focus on scalability, governance, and reliability in production. I leverage MLflow and Apache Airflow for experiment tracking, automated retraining, and monitoring, while ensuring explainability, fairness, and regulatory compliance using SHAP. I enjoy collaborating with product, analytics, risk, and engineering teams to translate model outputs into actionable business insights.…I am an AI/ML Engineer and Data Scientist with 4 years of experience designing, developing, and deploying scalable data, machine learning, and AI solutions that transform large-scale data into actionable insights across banking, healthcare, and e-commerce. I am proficient in Python, R, and SQL, with hands-on experience using PyTorch, TensorFlow, scikit-learn, and Spark ML for predictive modeling, NLP, and risk analytics. I build robust data extraction, preprocessing, and feature engineering pipelines with PySpark, Pandas, and NumPy to prepare high-quality datasets for training, validation, and inference. I have hands-on experience deploying and operating ML models on AWS SageMaker, Azure Databricks, and containerized inference services, with a focus on scalability, governance, and reliability in production. I leverage MLflow and Apache Airflow for experiment tracking, automated retraining, and monitoring, while ensuring explainability, fairness, and regulatory compliance using SHAP. I enjoy collaborating with product, analytics, risk, and engineering teams to translate model outputs into actionable business insights.

Lakshmi Pulicharla

Data Scientist, AI Engineer, Full Stack Developer, +2

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I am an AI/ML Engineer and Data Scientist with 4 years of experience designing, developing, and deploying scalable data, machine learning, and AI solutions that transform large-scale data into actionable insights across banking, healthcare, and e-commerce. I am proficient in Python, R, and SQL, with hands-on experience using PyTorch, TensorFlow, scikit-learn, and Spark ML for predictive modeling, NLP, and risk analytics. I build robust data extraction, preprocessing, and feature engineering pipelines with PySpark, Pandas, and NumPy to prepare high-quality datasets for training, validation, and inference. I have hands-on experience deploying and operating ML models on AWS SageMaker, Azure Databricks, and containerized inference services, with a focus on scalability, governance, and reliability in production. I leverage MLflow and Apache Airflow for experiment tracking, automated retraining, and monitoring, while ensuring explainability, fairness, and regulatory compliance using SHAP. I enjoy collaborating with product, analytics, risk, and engineering teams to translate model outputs into actionable business insights.…I am an AI/ML Engineer and Data Scientist with 4 years of experience designing, developing, and deploying scalable data, machine learning, and AI solutions that transform large-scale data into actionable insights across banking, healthcare, and e-commerce. I am proficient in Python, R, and SQL, with hands-on experience using PyTorch, TensorFlow, scikit-learn, and Spark ML for predictive modeling, NLP, and risk analytics. I build robust data extraction, preprocessing, and feature engineering pipelines with PySpark, Pandas, and NumPy to prepare high-quality datasets for training, validation, and inference. I have hands-on experience deploying and operating ML models on AWS SageMaker, Azure Databricks, and containerized inference services, with a focus on scalability, governance, and reliability in production. I leverage MLflow and Apache Airflow for experiment tracking, automated retraining, and monitoring, while ensuring explainability, fairness, and regulatory compliance using SHAP. I enjoy collaborating with product, analytics, risk, and engineering teams to translate model outputs into actionable business insights.

Available to hire

I am an AI/ML Engineer and Data Scientist with 4 years of experience designing, developing, and deploying scalable data, machine learning, and AI solutions that transform large-scale data into actionable insights across banking, healthcare, and e-commerce. I am proficient in Python, R, and SQL, with hands-on experience using PyTorch, TensorFlow, scikit-learn, and Spark ML for predictive modeling, NLP, and risk analytics. I build robust data extraction, preprocessing, and feature engineering pipelines with PySpark, Pandas, and NumPy to prepare high-quality datasets for training, validation, and inference.

I have hands-on experience deploying and operating ML models on AWS SageMaker, Azure Databricks, and containerized inference services, with a focus on scalability, governance, and reliability in production. I leverage MLflow and Apache Airflow for experiment tracking, automated retraining, and monitoring, while ensuring explainability, fairness, and regulatory compliance using SHAP. I enjoy collaborating with product, analytics, risk, and engineering teams to translate model outputs into actionable business insights.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Work Experience

AI/ML Engineer at Central Bank

February 1, 2025 - Present

Designed and deployed AI and machine learning 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 models using AWS SageMaker, supporting batch and near real-time risk scoring workflows across large-scale transactional datasets. Containerized ML and inference services with Docker, and provisioned cloud infrastructure using Terraform. Implemented CI/CD pipelines for ML workflows, integrated SageMaker endpoints with Lambda for event-driven decisioning, and established monitoring and logging for reliability and governance in regulated environments. Applied statistical analysis and model evaluation to tune thresholds and monitor behavior over time.

AI/ML Engineer at BJC Healthcare

May 1, 2024 - January 31, 2025

Designed and developed machine learning and NLP models for healthcare use cases such as patient risk stratification, readmission prediction, and clinical decision support using Python and scikit-learn. Built deep learning models (LSTM, CNN) for structured and unstructured EHR data, and developed AI-driven conversational and summarization solutions for secure EHR querying. Curated healthcare datasets on Azure, implemented NLP pipelines for clinical entity extraction with spaCy and transformers, and established CI/CD on Azure Databricks with MLflow for reproducible experiments. Automated retraining and deployment workflows with Apache Airflow and Kafka-based data ingestion.

ML Engineer at Wells Fargo

January 1, 2023 - November 30, 2023

Developed and productionized ML models for banking use cases including credit risk assessment, fraud detection, and customer analytics. Built end-to-end Python pipelines for data preparation, training, validation, and batch inference; implemented RESTful inference services via Flask; integrated CI/CD pipelines for automated testing and deployment with versioning and rollback. Implemented monitoring and logging to track model execution and data drift, and collaborated with compliance teams to ensure governance and audit readiness.

Data Engineer at Myntra

August 1, 2021 - December 31, 2022

Developed Python-based ETL/ELT pipelines for data ingestion, transformation, and validation, enabling ML-ready data for personalization and analytics. Optimized PySpark transformations and SQL queries for high-volume e-commerce data (orders, inventory, pricing, clickstream). Built Spark-based data processing jobs, integrated data sources into AWS S3 and Snowflake, implemented data quality checks, and created near real-time ingestion pipelines with Spark and AWS Lambda. Exposed curated datasets via REST APIs for BI tools and analytics teams.