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I’m an AI/ML Engineer with 2+ years of experience building and deploying machine learning and generative AI solutions across cloud environments. I specialize in LLM applications, Retrieval-Augmented Generation (RAG), and end-to-end MLOps, including model lifecycle management, evaluation, and monitoring. I focus on turning advanced AI capabilities into measurable business impact—improving forecasting/model performance, reducing deployment and retraining time, and strengthening data quality using Python, LangChain, MLflow, Docker, and AWS/Azure.…I’m an AI/ML Engineer with 2+ years of experience building and deploying machine learning and generative AI solutions across cloud environments. I specialize in LLM applications, Retrieval-Augmented Generation (RAG), and end-to-end MLOps, including model lifecycle management, evaluation, and monitoring. I focus on turning advanced AI capabilities into measurable business impact—improving forecasting/model performance, reducing deployment and retraining time, and strengthening data quality using Python, LangChain, MLflow, Docker, and AWS/Azure.

Divy Patel

AI Engineer, Data Scientist, Software Engineer, +1

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I’m an AI/ML Engineer with 2+ years of experience building and deploying machine learning and generative AI solutions across cloud environments. I specialize in LLM applications, Retrieval-Augmented Generation (RAG), and end-to-end MLOps, including model lifecycle management, evaluation, and monitoring. I focus on turning advanced AI capabilities into measurable business impact—improving forecasting/model performance, reducing deployment and retraining time, and strengthening data quality using Python, LangChain, MLflow, Docker, and AWS/Azure.…I’m an AI/ML Engineer with 2+ years of experience building and deploying machine learning and generative AI solutions across cloud environments. I specialize in LLM applications, Retrieval-Augmented Generation (RAG), and end-to-end MLOps, including model lifecycle management, evaluation, and monitoring. I focus on turning advanced AI capabilities into measurable business impact—improving forecasting/model performance, reducing deployment and retraining time, and strengthening data quality using Python, LangChain, MLflow, Docker, and AWS/Azure.

Available to hire

I’m an AI/ML Engineer with 2+ years of experience building and deploying machine learning and generative AI solutions across cloud environments. I specialize in LLM applications, Retrieval-Augmented Generation (RAG), and end-to-end MLOps, including model lifecycle management, evaluation, and monitoring.

I focus on turning advanced AI capabilities into measurable business impact—improving forecasting/model performance, reducing deployment and retraining time, and strengthening data quality using Python, LangChain, MLflow, Docker, and AWS/Azure.

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Language

Work Experience

AI/ML Engineer at DXC Technology, PA

February 1, 2026 - Present

Built a Retrieval-Augmented Generation (RAG) solution using LangChain, OpenAI APIs, and a vector database, improving internal knowledge retrieval accuracy by 11% during pilot deployment. Engineered automated ML pipelines with MLflow and Docker to reduce model retraining cycle time by 9% and improve reproducibility across teams. Deployed a FastAPI-based inference service on AWS, lowering average prediction latency by 8%. Optimized feature engineering and hyperparameter tuning, increasing pilot model F1 scores by 7% on classification tasks. Created explainable AI reporting and Power BI dashboards to help stakeholders identify key drivers and shorten model review cycles by 12%.

AI/ML Engineer at DXC Technology

February 1, 2026 - Present

Led development of a Retrieval-Augmented Generation (RAG) solution using LangChain, OpenAI APIs, and a vector database, improving internal knowledge retrieval accuracy by 11% during a pilot. Built automated ML pipelines with MLflow and Docker to reduce model retraining cycle time by 9% and improve reproducibility across teams. Orchestrated a FastAPI inference service on AWS, reducing average prediction latency by 8% for a customer-facing analytics application. Improved feature engineering and hyperparameter tuning workflows, increasing pilot classification F1 scores by 7%. Created explainable AI reporting and Power BI dashboards, helping stakeholders identify key drivers and shortening model review cycles by 12%.

ML Engineer at Infinite Infolab, India

August 1, 2022 - July 1, 2024

Developed predictive machine learning solutions with Python, scikit-learn, and SQL, improving classification accuracy by 19% across multiple client projects. Built ensemble models for demand forecasting, improving forecast accuracy by 24% versus legacy methods. Designed ETL and feature pipelines with Python and Snowflake, reducing data preparation effort by 28%. Deployed deep learning and time-series solutions on Azure, cutting manual planning time by 18%. Integrated Hugging Face Transformers for text analytics, improving document classification precision by 20%. Implemented CI/CD practices using Git and Docker, shortening model release cycles by 22%. Used statistical testing and A/B experimentation to validate model improvements, raising engagement metrics by 14%. Mentored junior analysts on MLOps and model evaluation, improving first-pass deployment success by 16%.

ML Engineer at Infinite Infolab

August 1, 2022 - July 1, 2024

Developed predictive machine learning solutions using Python, scikit-learn, and SQL to improve classification accuracy by 19% and accelerate decision-making across client projects. Built ensemble learning models for demand forecasting, improving forecast accuracy by 24% versus legacy methods. Designed ETL and feature pipelines using Python and Snowflake, reducing data preparation effort by 28%. Deployed deep learning and time-series solutions on Azure, cutting manual planning time by 18%. Integrated Hugging Face Transformer models into text analytics, improving document classification precision by 20%. Established CI/CD practices with Git and Docker to shorten release cycles by 22%. Used statistical testing and A/B experimentation to validate model enhancements, increasing engagement metrics by 14%. Mentored junior analysts on MLOps and model evaluation, improving first-pass deployment success by 16%.