Highly experienced Principal Machine Learning Engineer with extensive expertise in Machine Learning, Generative AI, Data Pipelines, and ERP systems. Skilled in designing and implementing client-server and n-tier applicat…
Highly experienced Principal Machine Learning Engineer with extensive expertise in Machine Learning, Generative AI, Data Pipelines, and ERP systems. Skilled in designing and implementing client-server and n-tier applications, with a strong proficiency in RDBMS and SQL programming. Proven track record in developing scalable solutions and driving innovation in AI technologies.
•Designed and developed a multi-agentic application for code generation and execution using the AutoGen framework with GPT-3.5-turbo and GPT-4.
•Integrated and applied Gemini 1.5 Pro multimodal LLM and Gemini Vision for advanced image and PDF preprocessing.
•Spearheaded the integration of Generative AI and LLMs, leveraged GPT 3.5-turbo / GPT-4 and Langchain to generate claims summaries reducing manual effort and time by 70%.
•Developed a framework to evaluate LLMs and Generative AI models using ROUGE, STS Transformer, BERT Score, and OpenAI prompts.
•Led the end-to-end development of a robust Machine Learning Apache Airflow pipeline in PySpark, encompassing data preprocessing, model training, tuning, and deployment to Google BigQuery. Leveraged PySpark variants of SARIMAX and FBProphet to generate millions of predictive models for sales forecasting over multiple years
•Implemented robust monitoring and maintenance protocols for a sales forecasting system, ensuring continuous performance and stability through proactive monitoring and timely maintenance .
•Led the design and development of an ETL pipeline in Apache Airflow, incorporating configuration-driven validations using Dynaconf. Developed a custom validation engine on Google BigQuery for post- processing of model output, ensuring data integrity and accuracy.
•Engineered a CI/CD pipeline utilizing Jenkins, SonarQube, and JFrog Artifactory, ensuring comprehensive unit test coverage and build failure mechanisms for automated deployments of Machine Learning artifacts.
•Engineered and deployed a custom, rules-based, and configurable Model Selection Engine utilizing Dynaconf and Google BigQuery, effectively assessing competing models.
•Led the development of a Machine Learning pipeline in PySpark using algorithms like Prophet and SARIMAX for sales prediction.
•Managed end-to-end deployment and integration of ML models in production environments. •Applied ML transformers and estimators for data preprocessing, landing processed data in GCS and BigQuery.
Designed and developed a multi-agentic application for code generation and execution using the AutoGen framework with GPT-3.5-turbo and GPT-4.
Integrated and applied Gemini 1.5 Pro multimodal LLM and Gemini Vision for advanced image and PDF preprocessing.
Spearheaded the integration of Generative AI and LLMs, leveraged GPT 3.5-turbo / GPT-4 and LangChain to generate claims summaries reducing manual effort and time by 70%.
Developed a framework to evaluate LLMs and Generative AI models using ROUGE, STS Transformer, BERT Score, and OpenAI prompts.
Led the end-to-end development of a robust Machine Learning Apache Airflow pipeline in PySpark, encompassing data preprocessing, model training, tuning, and deployment to Google BigQuery.
Leveraged PySpark variants of SARIMAX and FBProphet to generate millions of predictive models for sales forecasting over multiple years.
Implemented robust monitoring and maintenance protocols for a sales forecasting system, ensuring continuous performance and stability through proactive monitoring and timely maintenance.
Led the design and development of an ETL pipeline in Apache Airflow, incorporating configuration-driven validations using Dynaconf.
Developed a custom validation engine on Google BigQuery for post-processing of model output, ensuring data integrity and accuracy.
Engineered a CI/CD pipeline utilizing Jenkins, SonarQube, and JFrog Artifactory, ensuring comprehensive unit test coverage and build failure mechanisms for automated deployments of Machine Learning artifacts.
Engineered and deployed a custom, rules-based, and configurable Model Selection Engine utilizing Dynaconf and Google BigQuery, effectively assessing competing models.
Led the development of a Machine Learning pipeline in PySpark using algorithms like Prophet and SARIMAX for sales prediction.
Managed end-to-end deployment and integration of ML models in production environments.
Applied ML transformers and estimators for data preprocessing, landing processed data in GCS and BigQuery.
Migrated ETL pipelines from Azure Data Factory and Databricks to Astronomer/Airflow and GCP, handling hundreds of TB of data.
Developed a model for classifying tax entries to automate the reversal of accrued tax journal entries.
Built a pipeline for weekly retraining and real-time prediction using FastAPI.
Developed Web APIs for a kiosk system that helps pharmacists and patients pick medicines as prescribed.
Built functionality for pharmacists to fill bottles according to prescriptions and place them in tagged totes using Web APIs.
Worked on a Contract Management System with frameworks like WPF and MVC to make contracts using different configuration and data stored in MS SQL.
Developed a product used by finance companies of BMW, Mercedes Benz, and ISUZU globally.
Worked on an MVC based ERP to manage complete financials, security and enterprise mobility.
Implemented a multi-lingual interface in .NET.
Improved data usability using MS SQL and RDLC reporting.
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