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AI/ML Engineer with 4 years of experience building machine learning, NLP, and Generative AI solutions in enterprise environments. Hands-on in Retrieval-Augmented Generation (RAG), recommendation systems, forecasting, and scalable data pipelines using Python, SQL, PySpark, and modern AI frameworks. Experienced in MLOps practices including experiment tracking, feature engineering, evaluation, deployment automation, and monitoring. Proficient with LangChain, Databricks, MLflow, FastAPI, Docker, and Scikit-learn, collaborating cross-functionally to deliver reliable, production-ready AI systems.…AI/ML Engineer with 4 years of experience building machine learning, NLP, and Generative AI solutions in enterprise environments. Hands-on in Retrieval-Augmented Generation (RAG), recommendation systems, forecasting, and scalable data pipelines using Python, SQL, PySpark, and modern AI frameworks. Experienced in MLOps practices including experiment tracking, feature engineering, evaluation, deployment automation, and monitoring. Proficient with LangChain, Databricks, MLflow, FastAPI, Docker, and Scikit-learn, collaborating cross-functionally to deliver reliable, production-ready AI systems.

Lakshmi Lahari Satti

AI Engineer, Data Scientist, Data Engineer, +3

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AI/ML Engineer with 4 years of experience building machine learning, NLP, and Generative AI solutions in enterprise environments. Hands-on in Retrieval-Augmented Generation (RAG), recommendation systems, forecasting, and scalable data pipelines using Python, SQL, PySpark, and modern AI frameworks. Experienced in MLOps practices including experiment tracking, feature engineering, evaluation, deployment automation, and monitoring. Proficient with LangChain, Databricks, MLflow, FastAPI, Docker, and Scikit-learn, collaborating cross-functionally to deliver reliable, production-ready AI systems.…AI/ML Engineer with 4 years of experience building machine learning, NLP, and Generative AI solutions in enterprise environments. Hands-on in Retrieval-Augmented Generation (RAG), recommendation systems, forecasting, and scalable data pipelines using Python, SQL, PySpark, and modern AI frameworks. Experienced in MLOps practices including experiment tracking, feature engineering, evaluation, deployment automation, and monitoring. Proficient with LangChain, Databricks, MLflow, FastAPI, Docker, and Scikit-learn, collaborating cross-functionally to deliver reliable, production-ready AI systems.

Available to hire

AI/ML Engineer with 4 years of experience building machine learning, NLP, and Generative AI solutions in enterprise environments. Hands-on in Retrieval-Augmented Generation (RAG), recommendation systems, forecasting, and scalable data pipelines using Python, SQL, PySpark, and modern AI frameworks.

Experienced in MLOps practices including experiment tracking, feature engineering, evaluation, deployment automation, and monitoring. Proficient with LangChain, Databricks, MLflow, FastAPI, Docker, and Scikit-learn, collaborating cross-functionally to deliver reliable, production-ready AI systems.

Skills

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Expert

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Work Experience

AI/ML Engineer at NVIDIA

January 1, 2026 - Present

Built retrieval-augmented AI applications using Python, LangChain, and vector databases, reducing engineering document search time by 40% and improving team access to technical knowledge. Improved prompt evaluation and retrieval testing workflows, increasing response relevance by 18% for LLM-powered support and documentation use cases. Developed PySpark and Databricks pipelines to process structured and unstructured data, reducing model preparation time by 30%. Enhanced inference performance via request batching, caching, and efficient GPU utilization, lowering average response latency by 25%. Adopted MLflow for experiment tracking and model versioning, shortening validation cycles by 20% and improving reproducibility. Automated deployments with Docker and CI/CD pipelines, reducing manual release effort. Partnered cross-functionally to decrease manual analysis activities by 22%, and designed production monitoring dashboards for retrieval effectiveness, model quality, and inference metr

AI/ML Engineer at Persistent Systems

May 1, 2021 - November 1, 2023

Developed recommendation models in Python and Scikit-Learn, increasing user engagement by 16% through behavioral analysis and personalized content. Implemented NLP pipelines for document classification and entity extraction, reducing manual processing effort by 35%. Improved forecasting accuracy by 14% using XGBoost with feature engineering and structured validation. Automated data preparation workflows with PySpark and Airflow, reducing model training time by 28% while improving consistency. Built FastAPI-based prediction services to expose outputs via reusable APIs. Improved model reliability using hyperparameter tuning, error analysis, and periodic retraining. Created Power BI dashboards to track model performance and operational KPIs, improving stakeholder visibility. Collaborated with analysts and business teams to prioritize AI initiatives that improved operational efficiency and customer experience.

ML Engineer at Persistent Systems

June 1, 2020 - April 1, 2021

Built predictive models using Python, SQL, and Scikit-Learn to support operational reporting and improve forecast reliability. Standardized data cleansing and feature engineering processes, reducing preprocessing effort by 30% across projects. Developed reusable ETL scripts with Python and SQL to accelerate data availability for analytics and model development. Assisted in deploying ML services using FastAPI and Docker for consumption via internal prediction APIs. Performed exploratory data analysis on large datasets to identify trends and anomalies informing model development. Supported recommendation and forecasting efforts through dataset validation, model evaluation, and performance reporting. Prepared technical documentation for model assumptions, validation, and deployment procedures to improve knowledge sharing, and collaborated with senior engineers on end-to-end production ML workflows.