Apr 2024 – Dec 2024 · 8 mos · Bengaluru, Karnataka, India · Remote

• Led Gen AI vertical for space management — natural language to SQL conversion and seat recommendations.
• Learning: RAG pipelines, LLM fine-tuning (LoRA/QLoRA), model-agnostic AI for OpenAI, Gemini, and
• open-source LLMs.
• Contribution: Built LLM-agnostic RAG for SQL generation; fine-tuned LLaMA 8B; developed seat
• recommendation model.
• Achievements: Reduced analyst query dependency by 80%; secured commitments from 5 clients
• for on-boarding. AI Initiatives for in house products

Sep 2023 – Feb 2024 · 5 mos · Delhi

• I took a break to up-skill myself in the field of Generative AI, Created many POC projects and wrote articles on Medium

Sep 2022 – Aug 2023 · 11 mos · Gurugram, Haryana, India · Remote

• I was responsible to set up data science unit & AWS SageMaker pipelines for TE Connectivity’s ICT
• division.
• Learning:AWS MLOps workflows, predictive modeling, business dashboard integration.
• Contribution: Built end-to-end SageMaker pipelines; developed product life cycle models reducing
• production costs; created freight analytics dashboards.
• Achievements: Improved decision-making efficiency; delivered measurable cost savings.
• Key Technical Optimizations: Automated SageMaker deployments, Tableau integration for real-time insights, optimized data ingestion workflows.

Oct 2021 – Sep 2022 · 11 mos · United States · Remote

• Responsibility: Delivered AI solutions for dynamic pricing, customer service automation, and growth
• analytics.
• Learning: Enhanced skills in dynamic pricing models, knowledge graph-based NLP, and automated
• data pipelines.
• Contribution: Built a Dynamic Pricing Model using real-time data (+30% revenue); developed a
• knowledge graph chatbot in Python to automate Q&A; created web crawling pipelines and
• analytics dashboards.
• Achievements: Boosted revenue, reduced manual service workload, and provided actionable
• business insights.
• Key Technical Optimizations:
• Semantic knowledge graph design, automated large-scale crawling with error handling, interactive
• dashboards for KPI tracking.

Aug 2020 – Nov 2021 · 1 yr 3 mos · Delhi, India

• Worked on the following products
• HT News:
• Recommendation of news-based Timeline and Chronology of events (Potential Patent with HT):
• Created a knowledge graph-based recommendation system for timeline and chronology. This has been filed for a patent with HT Media. This was built on Neo4j
• Lead a 5-member team and developed the data crawling infrastructure for HT news on an AWS Ec2 sequence.
• Integrated 22 news sites for crawling news and ingesting into the database. Created a machine learning model for Sentiment Analysis with 83% accuracy using Bert algorithm.
• OTT Play:
• Data Engineering:
• 2. Created a separate architecture to crawl 22 Singapore OTT’s and hosted it on an Ec2 sequence.
• 3. Wrote 22 Singapore crawlers and implemented them on the architecture. Overall, till date, I have implemented 50 crawlers
• 4. Ran the downstream process on the data which consists of fetching the cast, crew, and poster URLs for the movies and shows.
• 5. Wrote an algorithm to re-fetch the missing IMDB ID’s, TMDB ID’s, corrupt posters and missing cast and crews for movie titles and show titles.
• 6. Created a de-duplication algorithm using Jaccard Probability to remove the duplicate records.
• 8. Worked on Azure data bricks and wrote aggregation logic for various data jobs using spark-sql to run the recommendation pipeline.
• Data Science:
• 1. Created a movie recommendation model which consists of the following:
• a. Created a new taxonomy structure for movies to improve similar movie recommendations.
• b. Used the user analytics data with the existing movie data to generate movie recommendations for a particular user using a collaborative filtering method.
• 2. Created a movie news classification model using the Bert algorithm with 90% accuracy:
• a. Used different fields like genres, likes, dislikes, and taxonomy structure as a single string to generate word embeddings using Bert.

Apr 2020 – Aug 2020 · 4 mos

• 1. Spearheaded the data crawling and data analysis of ticket pricing, bus times, bus routes, etc which helped
• to increase the revenue by 40%.
• 2. Implemented dynamic pricing pipelines to utilize real time data analytics and adjust the seat pricing
• according to the occupancy.

Feb 2020 – Mar 2020 · 1 mo

Jul 2019 – Oct 2019 · 3 mos · gurugram