Headline

Senior Data Scientist at BMC Helix | xNIQ | xAmazon

About

Machine Learning Engineer skilled in developing AI/ML products with 5 years of experience creating machine learning models and retraining systems and building data science prototypes to production-grade solutions. Complex problem-solver with analytical and driven mindset. Experience with core Data Science techniques.Effective customer-facing communication and listening skills.Expertise in architecting and rolling out new technology and solution initiatives. Building Machine Learning Algorithms, Deep Learning and Neural Networks. Currently working with Yolov7 and Detectron2 for Object Detection and Spacy for NER

Location

Pune

AI stack

anthropic apis

antigravity

claude code

claude

codex

cursor

deepseek

gemini

github copilot

langchain

langgraph

langsmith

mcp

notebooklm

ollama

openai

perplexity

Work experience

Senior Data Scientist at BMC Helix, Feb 2025 - Present · 1 yr 6 mos, Pune

Senior Machine Learning Engineer at NielsenIQ, Jun 2023 - Feb 2025 · 1 yr 9 mos, India, • On this GenAI Conversational System project, I was responsible for designing and developing a modular, scalable chatbot system for a client team. The system automated data extraction from internal databases to resolve client queries, reducing resolution time from 7 days to 1 day • To ensure extensibility, I developed a modular architecture using LangChain’s LangGraph, allowing the chatbot to dynamically acquire new skills and improve over time. This facilitated addressing a broader range of complex queries. • I built the backend with FastAPI, implementing REST API endpoints and WebSockets for real‑time communication. To maintain enterprise‑ level security, I integrated Azure Active Directory with JWT‑based authentication. • I collaborated closely with front‑end engineers to deploy the system using the Angular framework, ensuring smooth interaction between the chatbot interface and backend services. • I led the deployment process using Docker, ensuring scalability and ease of integration into the client’s environment. The system was secured and deployed on Azure Cloud, following best practices for cloud architecture. • The system reduced the reliance on internal database experts and significantly improved the client engagement experience by automating query responses, which previously required manual input. • AI Skills: Machine Learning, Natural Language Processing, Generative AI, Conversational AI, Large Language Models (LLM), LangChain, Lang‑ Graphs, FastAPI, OpenAI, Azure AD, JWT, WebSockets, Model Deployment, Python. • Soft Skills: Leadership, Team Collaboration, Project Management, Time Management, Communication. • Collaboration Tools: Git, Azure DevOps, JIRA, Confluence. • Cloud Platforms: Azure, Docker, FastAPI, Angular

Machine Learning Engineer at NielsenIQ, Jun 2021 - Jun 2023 · 2 yrs 1 mo, Maharashtra, India, I successfully developed three production models, including two Computer Vision models and one Natural Language Processing (NLP) model. • To improve the accuracy of product categorization, I utilizedGoogle Vision API to extract words and their bounding boxes from product images, which served as input data for the NLP model. Using spaCy, I built an NER model that effectively extracted key information from product descriptions, achieving an F1 score of 94% on the test set. • To improve the accuracy and performance of the models, I created ground truth data for NLP and object detection models using the Doccano and CVAT annotation tools. This involved managing the data upload, creating workflows, and training annotators on how to complete annota‑ tion jobs. • I also built a product category model using BERT, which significantly improved the accuracy of product classification, achieving an F1 score of 96% on the test set and enabling more efficient processing of product data. Additionally, I built and fine‑tuned an object detection model using Yolov7 pyTorch model, which achieved an accuracy of 85%. The training process involved optimizing various hyper parameters and data augmentation techniques to improve the model’s performance. • To ensure seamless integration with the existing workflow and easy deployment to the cloud, I deployed the production models using FastAPI and Docker. Model were trained, monitored and severed Azure ML and MLFlow • AI Skills: Machine Learning, Computer Vision, Natural Language Processing, Deep Learning, Image Processing, Object Detection, Data Annota‑ tion, Model Deployment, Python, PyTorch, spaCy, BERT, Yolov7, FastAPI, Docker, NumPy, OpenCV, Pandas, Scikit‑learn. • Soft Skills: Teamwork, Time Management, Communication, Presentation skills. • Collaboration Tools: Git, BitBucket, Confluence, JIRA, Jenkins • Cloud Platforms: Azure, Google Cloud

Data Analytics Engineer at Amazon, Dec 2015 - Jun 2021 · 5 yrs 7 mos, India, Collaborated with internal and external stakeholders to optimize data sourcing pipelines and verify data quality. Performed preemptive resolution of technical issues by utilizing detailed knowledge of ongoing processes and policies. Managed customer experience metrics, tracking deficits in service feedback and addressing required changes with appropriate personnel. Acted as liaison between clients and technical associates, translating complex ideas into easy-to-understand explanations. Developed polished visualizations to share results of data analyses. Improved data pipelines. Compiled, cleaned and manipulated data for proper handling.

Education

Computer Technology, Computer, University of Mumbai