Yarnit

Head of Artificial Intelligence

LTI - Larsen & Toubro Infotech

Head of Data Science Product Development & Consulting 

- Built an org that developed custom data science solutions in consulting engagement
- Built an AIML product and engineering pod at Lumin – A B2B NLP based Virtual Investigative Analytics product
- Coached and mentored a team of data scientists and analysts to maintain high productivity and low attrition
- Evangelised AIML roadmap amongst sales teams resulting in increased pitches and demos
- Recognised as an analytics translator for senior leadership and customers

Key solutions delivered
- An e-commerce brand pricing and opportunity insights product for a large FMCG player deployed for 11 markets highlighting opportunities >$ 500 Mn
- An NLG based narrative engine for a leading technology company resulting in improved engagement of the insights platform
- Managed Upsell/Cross-sell campaign strategy for a multi-national African retail bank increasing campaign response rates by >6%
- Designed and developed an anomaly detection product suite for a major drug manufacturer saving >10000 FTE hours annually 
- Pioneered adoption of Agile methodology  to release features on Lumin like data summarizations, correlations, forecasting, regression based simulation,  deep-dive diagnostics, anomaly detection on Lumin 
- Led the development of Nudges - a push based autonomous business insights engine

Associate Principal

Lymbyc - An LTI Company

- Owner of  data science consulting vertical
- Envisaged and prototyped Leni -  product acquired by LTI (rebranded as Lumin)
- Built, coached and mentored a team of young data scientists and analysts
- Spearheaded consulting engagements of over $2 Mn with a B2B2C e-commerce marketplace, an Indian Microfinance company and a US hospital network amongst others
- Delivered business solutions in sales forecasting, segmentation, price optimization, route optimization, acquisition models, risk scorecard etc.
- Promoted product in NASSCOM conferences & maintained relationships with academia as a visiting faculty and interview panel member for SCMHRD’s MBA (BA) program

Director

GENPACT

Responsible for managing risk engagement with a large European financial services provider

• Led a team of analysts managing credit risk reporting and scorecard development across UK, Benelux and Austria

• Developed, implemented and monitored acquisition (A-score) and portfolio (B-score) risk scorecards along with segmentation based risk strategies for a variety of lending products like personal loans, auto loans and leases, credit cards and structured finance products

•In the scenario of a dismal cross-sell rate developed a cross sell response model resulting in an increment of >15% response rate

• Conducted secondary research to identify the issues and opportunities in the European consumer and commercial finance markets 

• Leveraged the sales team to initiate campaigns aimed at risk managers in leading European banks and financial institutions

Manager

HSBC Card Services

Marketing Analytics support to HSBC North America Retail Private Label Credit Cards division for the Premium and Discount Departmental Store Credit Cards

Developed Profit Based Segmentation strategies for retail card portfolios to maximize returns on marketing investment.

Conducted deep-dive analyses on four of the biggest retail store credit portfolios on a regular basis.

Consulting initiatives for merchant partners basis customer loyalty to design 'win-win' campaigns for the bank and the merchant

Led process standardization initiatives

Subject Matter Expertise in Co-branded credit cards

Analyst

Modelytics

Operational - Worked accross consumer credit products ranging from Credit Cards, Mortgage loans, Auto loans and Niche Speciality Loans

Functional - Worked in Risk and Marketing Analytics functions

Project Management - Fraud strategy for one of the largest banks in the US in the speciality loan segment.Developed Scorecards to mimick the IRS’ tax refund application scrutiny process to identify frauds in the income tax refund anticipatory loans portfolio

Information based customer value management for a leading credit card issuer in the US. Developed Pricing Decison Engine to help pricing decision making for new acquisitions to maximise customer lifetime value.

Developed retention strategy on a sub-prime mortgage portfolio to improve profitability in a competitive business scenario, while mitigating potential losses


Client Managment - Client-site deputation for a period of six months for an ngagement with a leading US bank. Managed all client communication.

Product Development - Was involed in development of a mortgage related analytics solution

Business Development - Part of the pre-sales team developing pitches for mortgage related solutions

Skills acquired - Risk & Fraud Analytics, Statistical Scorecard development, Consumer Lending business understanding, Advanced SAS and MS Excel

Business Analyst

- Conceptualising and designing monthly campaign tracking dashboards for retention campaigns run by a large mortgage service provider in the US.

- Developed deep understanding of the mortgage business 

- Gained experience in predictive modelling through data regression.

- Developed expertise in using SAS 9 (Unix platform) for deep dive data analytics

- Was offered a permanent position at the end of the internship

Business Analyst (Intern)

Hi there! I'm Abhinav, a passionate data scientist and AI practitioner with over 15 years of experience in building AI and ML solutions. I have a strong background in consulting and application development, leveraging data and AI to solve business problems.

Currently, I am the co-founder and head of AI at Yarnit, where I lead a small but talented team in developing a content marketing platform leveraging Generative AI, recommendation systems and machine learning. Yarnit.app is revolutionising the way marketers create content.

I am constantly exploring the latest advancements in #AI #MachineLearning #DataScience #GenerativeAI #DataProducts #Analytics #LLMs #Technology #Education #EthicalAI. I am driven by a deep curiosity and a desire to push the boundaries of what's possible with AI.

Throughout my career, I have successfully led and delivered numerous AI and ML projects, ranging from marketing analytics and credit risk management to NLP and forecasting. My current focus lies in generative AI and LLM stack. I enjoy tackling complex challenges and transforming raw data into actionable insights.

Looking forward to creating innovative AI solutions and making a positive impact in the world through technology. Let's build the future together! 🚀

📌 "Retrieval Augmented Generation - A Simple Introduction"
Based on my experiments with RAG, this e-book talks about -

🔹 What is Retrieval Augmented Generation?
🔹 How does RAG help?
🔹 What are some popular RAG use cases?
🔹 What does the RAG Architecture look like?
🔹 What are Embeddings?
🔹 What are Vector Stores?
🔹 What are the best retrieval strategies?
🔹 How to Evaluate RAG outputs?
🔹 RAG vs Finetuning - What is better?
🔹 How does the evolving LLMOps Stack look like?
🔹 What is Multimodal RAG?
🔹 What is Naive, Advanced and Modular RAG?

Something to read over the weekend 😊 Do let me know what you think.

#generativeai #llm #llmops #rag #learning

Abhinav Kimothi

Co-founder | Head of AI @ Yarnit.app - GenAI powered Marketing Application

RETRIEVAL AUGMENTED GENERATION - A Simple Introduction

Paul Francis Steele ⊕

Retrieval Augmented Generation - A Simple Introduction for you weekend browsing 😊 

Over the last few weeks, I have been deeply exploring RAG for building a better brand identity in Yarnit. While the in-production application of RAG is complex, I have compiled some introductory notes that'll help you getting introduced to some paradigms and terms.

These include - 

⭐ What is Retrieval Augmented Generation? 
- The inherent problems of LLMs (like #gpt3 #llama2 ) that led to the popularity of RAG.

⭐ How does RAG help?
- How does RAG address the problems of hallucination and enables relevant context

⭐ What are some popular RAG use cases?
- RAG is being used from summarization to conversational agents to content generation.

⭐ What does the RAG Architecture look like?
- The indexing pipeline and the generation pipeline

⭐ How is data loaded and chunked?
- creating a context knowledge source using #langchain and #llamaindex 

⭐ What are Embeddings?
- conversion of text data in vector form using #openai and opensource embeddings from #huggingface 

⭐ What are Vector Stores?
- storage of embeddings in vector databases like #pinecone chromadb, weviate etc. and using vector indices like FAISS

⭐ What are the best retrieval strategies?
- simple to nuanced retrieval strategies for better context enables by #langchain and #llamaindex 

⭐ How to Evaluate RAG outputs?
- the complexities of evaluation and popular metrics and frameworks like Ragas and TruEra

⭐ RAG vs Finetuning - What is better?
- RAG and finetuning being complementary to each other

⭐ How does the evolving LLMOps Stack look like?
- Tools and frameworks to put RAG in production

⭐ What is Multimodal RAG?
- The growing popularity of Image+Text RAG

⭐ What is Naive, Advanced and Modular RAG?
- The SOTA in RAG and it's evolution

These notes largely focus on inference RAG. There are some research papers, blogs from thought leaders like Aman Chadha, Lillian Weng, Leonie Monigatti, Chip Huyen, and official documentation from HuggingFace, Pinecone, Trulens etc. continue to help me immensely in my journey with RAG.

A preview of the notes is attached here for you to go through. Some pages are missing that, if it interests you, you can download from Gumroad (linked in first comment) Hope you enjoy them.

I believe RAG will continue to play a significant role in operationalizing LLMs and Multimodal models for business. I'll be eager to hear what you think about these. Please also share resources that you come across in the context of RAG. 

#generatieveai #llmops #llms #retrievalaugmentedgeneration #gemini #genai 

Retrieval Augmented Generation - A Simple Introduction

Labour Day is a time to recognise the hard-fought battles for workers' rights. From the establishment of the eight-hour workday to safer working conditions, we've made significant progress together. As we take a well-deserved break, let's reflect on the work that still lies ahead in ensuring fair treatment, dignity, and equality for all workers. Wishing you a restful and joyful Labour Day! Let's continue to build a brighter, more inclusive future - together. #LabourDay 

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