Available to hire
Data Scientist with 5 years of experience in designing, developing, and deploying machine learning models and AI-driven
solutions. Proficient in Python, TensorFlow, PyTorch, Scikit-learn, and OpenCV, with expertise in deep learning, NLP, computer
vision, and MLOps. Skilled in end-to-end ML pipelines, including data preprocessing, model training, optimization, and
deployment using Docker, Kubernetes, and CI/CD. Experienced in cloud platforms (AWS, GCP, Azure), big data technologies
(Spark, Hadoop), and databases (SQL, NoSQL, MongoDB).
Skills
Language
English
Advanced
Work Experience
Data Scientist – Sub Lead at Highbrow Technology
April 1, 2024 - November 27, 2025Designed and executed supervised fine-tuning (SFT) pipelines using Hugging Face Transformers, PyTorch, and TensorFlow; boosted LLM contextual accuracy and relevance by ~20% across benchmark datasets and enterprise test cases. Developed Implicit Code Execution (ICE) modules to strengthen logical reasoning, symbolic problem-solving and mathematical precision, improving STEM-related task success rate by 25% in evaluation tests. Led Reinforcement Learning with Human Feedback (RLHF) using Proximal Policy Optimization (PPO) and reward modeling, enabling 30% reduction in critical reasoning errors through structured feedback loops and iterative optimization. Engineered adversarial testing frameworks to evaluate bias, hallucination frequency, and edge-case robustness, cutting high-severity error occurrences by 18% and ensuring production-readiness for real-world scenarios. Built automated error taxonomy frameworks leveraging Python, Pandas, and SQL to classify deficiencies, generate targeted sy
Data Engineer at Addgene
August 1, 2022 - August 1, 2022Implemented scalable ETL pipelines using Python, Django, and Apache Airflow, automating ingestion of 70+ structured and unstructured datasets from AWS S3, SQL Servers, APIs, and web sources, reducing manual data processing time by 35%. Orchestrated event-driven data workflows by integrating Airflow with AWS Lambda, Redshift, and S3, enabling real-time and batch data processing for analytics and ML pipelines. Optimized storage and querying of petabyte-scale datasets by leveraging Amazon Redshift Spectrum and Athena, improving query performance by 40% and lowering compute costs. Implemented monitoring and alerting systems using AWS CloudWatch, proactively identifying pipeline failures and ensuring 92% uptime for business-critical workflows. Engineered reproducible ML-ready datasets by managing environments with pip, setup.py, and requirements.txt, supporting Data Scientists and ML Engineers. Developed and deployed a machine learning PoC leveraging Python, Scikit-learn, and Pandas, achiev
Program Analyst at Cognizant Technology Solutions
April 1, 2020 - April 1, 2020Optimized relational databases using advanced SQL (joins, subqueries, indexing), improving query performance by 35% and enabling faster access to critical datasets for analytics and AI models. Developed ETL pipelines with Python, SQL, and Apache Airflow to ingest, clean, and transform data from structured/unstructured sources into cloud-based data warehouses, improving data availability for analytics by 40%. Built executive-level Tableau dashboards that visualized real-time KPIs (revenue trends, operational efficiency, and customer churn), reducing reporting turnaround time by 50% and enhancing data-driven decision-making. Automated real-time data ingestion by integrating third-party APIs using Python (Requests, Pandas), enabling continuous updates for machine learning pipelines used in predictive analytics (e.g., customer churn prediction, risk modeling). Implemented data governance and integrity frameworks through database normalization, validation checks, and schema design best prac
Technical Development - AI/ML, Data Science at KLEF
May 1, 2019 - May 1, 2019Designed, developed, and deployed a personalized recommendation engine using collaborative filtering, content-based filtering, autoencoders, and Large Language Models (LLMs), increasing sales by 15% and enhancing customer satisfaction. Implemented K-Means clustering for customer segmentation, improving recommendation precision. Optimized features for a deep learning-based Autoencoder recommender, achieving 92% accuracy in predicting user preferences. Conducted comprehensive data preprocessing and ETL pipeline development using Python (Pandas, NumPy, Scikit-learn, PySpark), Scala, SQL, and Apache Spark, ensuring high-quality, structured data for model training. Utilized C for implementing performance-critical components and system-level data handling modules within the pipeline, enhancing computational efficiency. Deployed ML models on AWS Cloud, leveraging SageMaker for model training, Lambda for serverless inference, Redshift for data warehousing, and CloudWatch & Prometheus for real-
Education
Master of Science at Northeastern University, Boston
January 11, 2030 - November 27, 2025Bachelor of Technology at Koneru Lakshmaiah University
January 11, 2030 - November 27, 2025Qualifications
AWS Certified Cloud Practitioner
January 11, 2030 - November 27, 2025MTA – Cloud Fundamentals
January 11, 2030 - November 27, 2025Industry Experience
Software & Internet, Life Sciences, Professional Services, Education, Media & Entertainment
Skills
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