Albertsons Companies

Engineered a scalable Log Analyzer Agent for Albertsons, leveraging Generative AI and MLOps principles to autonomously diagnose and fix system issues. The agent, built with a fine-tuned SonarCloud LLM, uses a Retrieval-Augmented Generation (RAG) system powered by Qdrant, Weaviate, and FAISS to intelligently retrieve logs and generate solutions. Integrated with OpenAI APIs, the solution automatically creates GitHub pull requests with code fixes, all while addressing key performance challenges in latency and throughput.

Developed and deployed Retrieval-Augmented Generation (RAG) systems for personalized user experiences and intelligent supply chain information. Integrated key technologies including Dense Passage Retrieval (DPR), LangChain, OpenAI APIs, and vector databases like FAISS.

Engineered an interactive emotional analysis application leveraging computer vision (using Mediapipe, OpenCV), OpenAI GPT-4, ElevenLabs, and DeepFaceLab. This project enhanced the customer experience by providing real-time emotion analysis, NLP-driven insights, and dynamic facial animation.

Built and scaled high-performance APIs with FastAPI and Flask, supporting full-stack features with React and Next.js. Streamlined model training and deployment workflows with Docker, Kubernetes, TensorFlow, PyTorch, and Hugging Face.

Managed the optimization and deployment of 1,400 models on Azure Databricks and GCP Cloud serverless architectures. Designed a scalable model wrapper and a job monitoring library to improve efficiency, while applying compression techniques to maximize GPU utilization and performance.

Senior Machine Learning Engineer

Cleveland Clinic

working on clustering techniques for analyzing data on pediatric patients with Amblyopia, I possess extensive knowledge of several machine learning models including Decision Trees, Random Forest, Support Vector Machines, Gradient Boosting, and Neural Networks. I have expertise in data preprocessing techniques like data cleaning, feature engineering, and dimensionality reduction, and have worked with several libraries including Pandas, Numpy, Scikit-learn, TensorFlow, Keras, PyTorch, NLTK, spaCy, Gensim, Matplotlib, Seaborn, Plotly, and Tableau. Additionally, I have experience using various cloud platforms and services such as AWS, Google Cloud, and Microsoft Azure, as well as containerization technologies such as Docker and Kubernetes. My strong background in statistics, data analysis, and programming in languages such as Python, R, and SQL allows me to effectively work with large and complex datasets to provide meaningful insights and develop innovative solutions.

Developed deep learning models for mask detection, crowd detection, animal detection for warehouse security, and on-off duty detection using AI-based high-tech cameras and Jetson Nano Kit. Leveraged GAN (Generative Adversarial Networks) to produce synthetic images, enhancing the accuracy of facial mask detection systems.

Demonstrated the ability to influence stakeholders by presenting data-driven insights and advocating for the adoption of advanced AI technologies.

Deployed ML models via AWS SageMaker, EMR, and S3 using GitHub, Jira, Jenkins ensuring seamless integration and scalability in cloud environments.

Utilized HTML, CSS, JavaScript, and React.js to construct live dashboards, enabling real-time visualization of data and tracking of key performance indicators (KPIs).

Employed MLflow to effectively track and manage machine learning experiments, ensuring reproducibility and optimization in model development and evaluation.

Machine Learning Engineer

U.S. Department of Veterans Affairs

Using decision trees, logistic regression, and neural networks, I have extensive experience working with a variety of other machine learning algorithms such as Random Forests, Gradient Boosting Machines, Support Vector Machines, and Deep Learning frameworks such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). I have also worked with various data preprocessing and feature engineering techniques such as normalization, scaling, dimensionality reduction, and one-hot encoding. Furthermore, I have experience working with various tools and libraries such as scikit-learn, TensorFlow, Keras, PyTorch, and Theano. Additionally, I have experience with statistical analysis, data visualization, and programming languages such as Python and R.

CapitalNow.in

In my role, I was responsible for developing an AI model for customer identity verification, which included designing text segmentation and extraction techniques for customer-provided documents such as ID cards, driving licenses, and passports. I tailored these techniques for over 100 countries worldwide to ensure accuracy and efficiency. Additionally, I utilized optimization and multi-processing techniques to reduce the pipeline time for data scraping from one year to less than one month, improving the overall workflow.

To ensure the security of the verification process, I employed multi-instance segmentation methods for detecting spoofed and tampered documents. Furthermore, I designed a model for face verification of customers that replaced the existing API, reducing company costs by 15%. I also optimized the existing model, decreasing the company's core production pipeline time by 25%, resulting in a more streamlined and efficient process.

Overall, my work in developing and optimizing the AI model for customer identity verification required a diverse range of skills, including machine learning, data processing and optimization, and knowledge of various libraries and tools such as multi-instance segmentation, face verification, and optimization techniques. These skills and experiences were critical in creating a robust and efficient verification process for the company.

Lead Data Scientist

Stratycon Business Solutions Pvt Ltd

•	Managed the Data Science team, designed the product pipeline, and transformed VoIP services through AI to reduce the need for human intervention, providing benefits to businesses with large call volumes.
•	Performed in-depth analyses of structured and unstructured data using advanced statistical techniques and strong knowledge of algorithms, resulting in the development of data reports, performance metrics, and strategic objectives to address business partner objectives in CVS.
•	Performed exploratory data analysis using pandas-profiling and K-Means clustering on NCES data for new business prospects to expand Annuities business.
•	Utilized AI/ML and cloud capabilities to build innovative solutions for complex business problems, resulting in improved analytical scenarios and potential future outcomes for 1-Pharma company.
•	Developed an algorithm using OpenCV, YOLO and TensorFlow for license plate detection which yielded an accuracy of 90%
•	Designed a system for 70+ branches of Dominos in the USA, including an algorithm to mask credit card information of customers from call recordings, a sentiment analysis system, an algorithm to detect up-selling, and a pipeline that allows AI-based systems to accept voice or text instructions and perform tasks accordingly.
•	Performed data wrangling, manipulation and visualization on 3 million rows of healthcare data for descriptive statistical analysis with R, Python, Tableau and Excel.
•As part of my responsibilities, I built an AI/ML enabled Cyber Security product that uses a hybrid neural network (a combination of supervised and unsupervised learning) to detect, handle, and remediate ransomware attacks, while also designing a machine learning pipeline to perform database interactions, data pre-processing, and track model behavior.

Data Scientist/Machine Learning Engineer

MERXIUS SOFTWARE PRIVATE LIMITED

•	Gathered business requirements, definition, and design of the data sourcing, worked with the data warehouse architect on the development of logical data models.
•	Collaborated with Data Engineers to filter data as per the project requirements.
•	Implemented various statistical techniques to manipulate the data (missing data imputation, principal component analysis, and sampling).
•	Applied different dimensionality reduction techniques like principal component analysis (PCA) and t-stochastic neighbor embedding (t-SNE) on feature matrix.
•	Identified outliers and inconsistencies in data by conducting exploratory data analysis (EDA) using python NumPy and Seaborn to see the insights of data and validate each feature.
•	Performed univariate and multivariate analysis on the data to identify any underlying pattern in the data and associations between the variables.
•	Performed feature engineering including feature intersection generating, feature normalize and label encoding with Scikit-learn preprocessing.
•	Developed and implemented predictive models using machine learning algorithms such as linear regression, classification, multivariate regression, Naive Bayes, Random Forests, K-means clustering, KNN.
•	Used clustering techniques like DBSCAN, K-means, K-means++ and Hierarchical clustering for customer profiling to design insurance plans according to their behavior pattern.
•	Used Grid Search and Random Search to evaluate the best hyper-parameters for my model and K-fold cross-validation technique to train my model for best results.
•	Worked with Customer Churn Models including Random forest regression, lasso regression along with pre-processing of the data.
•	Designed rich data visualizations to model data into human-readable form with Matplotlib.

Data Analyst

With 10+ years of experience in the field of machine learning, deep learning, natural language processing, and algorithmic trading. Skilled in designing and implementing ML systems at scale for Websites, Application, Devices, Automation, Azure Databricks, NLP, chatbots, AR/VR, CV,search, and recommender systems. Proficient in Time-series, Graphs, Geo-tagged, supervised, unsupervised learning and Reinforcement Learning techniques. Strong analytical and problem-solving skills, with proficiency in multiple programming languages and tools, including agentic AI and ROG model integration.

TECHNICAL SKILLS:

Languages: Python, C++, Java, Scala, HTML 5, CSS, JavaScript, Unix/Linux

Databases: RDBMS, SQL, DDL, NoSQL, Snowflakes,Kafka, Spark Streaming, Hadoop, HIVE

Frameworks: Flask, Django, REST API, Microservices, Spring framework

DevOps and API Tools: GIT, Docker, Kubernetes, Azure Devops, Postman, Git Hub Actions, Fast API, ArgoCD, Bazel

Cloud and Security Tools: AWS SageMaker, S3, EC2, EMR, Lex, Azure Databricks, Terradata, Event Hubs, Functions Kafka, Vertex-AI, Dialogflow

Others: Airflow, Data Robot, MLflow, KubeFlow,

Machine Learning Tools: LLMS, Variation Autoencoders, BERT, EMLO,Generative AI, Synthetic Data Generation, TensorFlow, Keras, HuggingFace, PyTorch, SpaCy, NLTK, NumPy, Pandas, Scikit, PySpark, PySQL, Gensim, CoreNLP, MLlib, GraphX, iPython, Langchain, Mlflow

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