About
Highly analytical and detail-oriented AI Engineer with a robust foundation in machine learning, deep learning, and generative AI, specializing in developing and deploying end-to-end ML pipelines and LLM-based solutions. Proficient in Python, TensorFlow, and cloud platforms, I leverage MLOps practices and modern AI frameworks to deliver scalable, high-impact solutions that drive operational efficiency and enhance decision-making.
Work
TCS
|Assistant System Engineer
Bangalore, Karnataka, India
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Summary
Led the development and maintenance of Python automation scripts, enhancing operational efficiency and data solution reliability within an Agile framework.
Highlights
Developed and maintained Python automation scripts, processing over 50,000 records daily, streamlining workflows and boosting operational efficiency by 20%.
Designed and implemented a Pytest-based data validation framework, ensuring 100% test coverage and reducing manual QA efforts by 3 hours each week.
Collaborated within an 8-member Agile team to deliver scalable, production-ready data solutions on schedule, contributing to timely project completion.
Introduced robust error handling and logging mechanisms, improving system reliability and reducing debugging time by 30%.
INEURON
|Data Science Intern (Full Stack Data Science Bootcamp 2.0)
Remote
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Summary
Executed end-to-end machine learning projects, from data collection to deployment, with a focus on model accuracy, MLOps, and production-ready API development.
Highlights
Completed over 15 end-to-end ML projects, achieving an average model performance exceeding 85% across diverse applications.
Built and deployed machine learning models using Random Forest and XGBoost, increasing prediction accuracy by 25% through systematic hyperparameter tuning with GridSearchCV and RandomSearchCV.
Developed robust REST APIs using Flask, enabling seamless model serving in a production environment.
Implemented MLOps practices, including MLflow and DVC, decreasing model deployment time by 50% through automated pipelines.
KPMG
|Data Analytics Intern
London, England, UK
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Summary
Analyzed customer data and developed segmentation models, providing actionable insights and improving reporting efficiency for marketing optimization.
Highlights
Analyzed customer demographic data from over 5,000 records using Python and Excel, identifying key business insights to inform strategic decisions.
Built a customer segmentation model using K-means clustering, effectively identifying distinct customer groups for targeted marketing campaigns.
Created an interactive Tableau dashboard with 8 visualizations, reducing reporting time from 2 days to 2 hours.
Delivered actionable recommendations for marketing optimization, specifically impacting New South Wales and Victoria regions.
Education
Roehampton University
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Master of Science
Data Science
Courses
Advanced Machine Learning
Deep Learning
Statistical Modelling
Big Data Analytics
AI Applications
JNTUHCEJ University
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Bachelor's
Electronics and Communication
Grade: 7.89/10
Courses
Object-Oriented Programming
Databases
Data Structures and Algorithms
Artificial Intelligence
Image Processing
Certificates
Machine Learning Course
Issued By
Stanford University
Tableau Certification
Issued By
Simplilearn
Artificial Intelligence in Python
Issued By
Great Learning
Python Certification
Issued By
Hacker Rank
Skills
Programming Languages
Python, SQL.
Machine Learning/Deep Learning
Scikit-Learn, TensorFlow, Keras, PyTorch, XGBoost, LightGBM, Supervised Learning, Unsupervised Learning, Neural Networks (ANN, CNN, RNN, LSTM, GRU), Computer Vision (YOLO, RCNN), NLP (BERT, GPT, Transformers).
Generative AI & LLM
LangChain, LangGraph, Llama Index, CrewAI, Autogen, Agno, Hugging Face, Vector Databases (Pinecone, Chroma DB, FAISS), RAG Architecture, Prompt Engineering, Fine-Tuning, Agentic AI.
Data Engineering
Pandas, NumPy, SciPy, Beautiful Soup, Apache Spark (basics), ETL Pipelines, Data Preprocessing, Feature Engineering, A/B Testing, Hypothesis Testing.
MLOps & DevOps
MLflow, DVC, Weights & Biases, Docker, Kubernetes (basics), CI/CD, GitHub Actions, Airflow, Model Monitoring, Model Versioning.
Cloud & Databases
AWS (EC2, S3, SageMaker), Google Cloud (basics), MySQL, PostgreSQL, MongoDB, Redis.
Visualization & Tools
Tableau, Power BI, Excel, Matplotlib, Seaborn, Plotly, Streamlit, Flask, FastAPI.
Methodologies
Agile, Git, JIRA, Test-Driven Development.