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Motivation

With a wealth of experience in Data Science and Machine Learning, I bring a unique blend of expertise in Research and Project Management. My professional journey in industries and in academia have been marked by a commitment to turning data into actionable insights. Throughout my career, I have not only developed cutting-edge models but have also been instrumental in designing robust architectures that power these solutions in real-world scenarios. My proficiency in AI and its applications has provided me with a deep understanding of data-centric and implementational challenges, enabling the creation of innovative solutions that bridge the gap between technical intricacies and end-user requirements. I am driven by the excitement of crafting intelligent systems and eager to contribute my expertise in shaping the future of generative AI. 

Let's create together, imagine beyond limits, and revolutionize the landscape of generative artificial intelligence.

Skills

Machine Learning/Deep Learning/GenAI
9

MMLLMs/LLMs/LVMs (AzureGPT-3.5/4.O, Gemini, Phi3, DALLE, ImaGen, YOLOs), BERTs, Flavours of Transformers (LongFormer, LinFormer), Bi-LSTM-CRFs and CNNs. Classic ML (e.g. SVMs, CRFs, Decision Trees). Transfer Learning, Multi-Modal Learning, Multi Task Learning and Reinforcement Learning.

Tools: Scikit-Learn, Tensorflow, PyTocrh, Huggingface, LangChain, LangGraph, LangFlow,  LLamaIndex, CrewAI.

Natural Language Processing
9

Language Identification, Part-of-Speech Tagging, Sentiment Analysis, Relation Extraction, Name Entity Recognition, Entity Linking, Question Answering, Long Document Parsing, Knowledge Graphs, Textual Entailment and LLM Prompt engineering,  Chatbots, RAG, Chains, Agents and Agent Prompting techniques (chain/tree-of-thought, plan-validate-action).

Devops and MLOps
7

Docker, Git, Jenkins, MLFlow, Databricks, DVC

ML Models Lifecycle Management, AzureDevOps (Basics) 

Cloud
7

AWS (AutoML, Sagemaker, EC2, S3, Lambda)

AZURE (Azure AI/ML Services)

GC (Vertex AI)

Programing Language
9

Python, Java, C++

Project Management Tools
8

JIRA: Scrum and KANBAN

Work History

Manager (AI/ML/GenAI)

Cognizant, Amsterdam
Feb 2024Present
  • Leading and managing AI/ML initiatives, focusing primarily on Generative AI. Driving the development and implementation of innovative AI/ML solutions, leveraging generative AI techniques to deliver cutting-edge products and services.
    • Cognitive SDLC: Lead ideator/developer a custom multi-role multi-agent (MRMA) platform to automate software development and research. Includes - auto coding, documentation and suggestion, testing, diagram understanding and project management plugins (JIRA, Confluence).  Tech Used: Gemini 1.5 Pro, Bison, GPTs (3.5 Turbo, 4.0), Langchain, Streamlit, VertexAI,  Agents: Zero-Shot, REACT,  PlanAction
    • KYC-BOT: KYC chatbot is for Banking and Financial Services. The chatbot is capable of interracting with databases, data collision handling, search and summarize from web for any given KYC forms in PDF and images. The chatbot is highly customizable and modular and can be adapted to many BFS use cases. Tech Used: Gemini 1.5 Pro, GPTs (3.5 Turbo, 4.0), Langchain, Streamlit, VertexAI, Agents: CSV, REACT,  Self-Ask-Search
    • Intelligent Tesintg Env: Client project. Co-development. This tesing environment is amied for banking environment and specific to data valut related testing cases. Tester can generate test cases with test data, query data bases and power designer, convert test scripts and generates documentation for tests.  Tech Used: AzureGPT-4.O,  AzureAISearch,  Langchain, Streamlit
    • GEN AI Accelaration Strategy : Gen AI enablement framework (define-align-design), with one of leading banking clients.  Enables Gen AI use case selection & creation in a reliable manner, risk assesment, gen ai backlog prioritization, platform setup and quick use case realization.      
  • Fostering a collaborative and inclusive work environment, promoting teamwork and synergy among team members to maximize productivity and achieve project goals.
    • Master Thesis Supervision(s)
    • Hackathon(s)
      • Solve with Google Princess Máxima Center's mission:  Communicate the steps and details of a medical treatment journey to kids through a comic book where the patient becomes the superhero and all the medical steps becomes gainable superpowers.  Tech Used: Gemini, ChatGPT-4, DALL-E 3, and ElevenLabs).
  • Providing expert consultation and guidance to clients, leveraging my deep understanding of AI/ML technologies and industry best practices. Actively contribute to the AI community through knowledge sharing, presenting at conferences, and participating in industry events. Follwoing are some of my interesting engagements: RABO Bank  - Cognizant Gen AI POC Tracks (FEB-MAR), KPN - Cognizant Gen AI Connects (Monthly), Cognizant Gen AI Use Case Realisation Framework, Cognizant RAG Evalutaion Framework 

Senior Data Scientist

Elsevier, Amsterdam
Aug 2018Dec. 2023
  • Lead the development of end-to-end machine learning solutions (Data, Training, Testing and Deployment) and services at Elsevier, employing state-of-the-art deep learning models (LLMs with LangChain, Huggingface Models), Databricks and AWS services such as Sagemaker.
  • Collaborate closely with colleagues to define project scope, establish objectives and deliverables, and manage projects using JIRA, employing both KANBAN and Scrum methodologies.
  • Demonstrated strong project management skills, overseeing tasks such as data creation, model development, testing, and deployment.
  • Maintained clear and concise documentation throughout the project, facilitating knowledge transfer and ensuring effective collaboration among team members.
  • Consistently met project deadlines and delivered high-quality results, contributing to the overall success of the team and organization.
  • Following is one of my nice projects:

    • LLM and BERT-based Automatic Scientific Article Publishing Pipeline:  Utilized LayoutLM Document AI and GPT One-shot Prompts with Langchain to enhance the efficiency and accuracy of the publishing process. Reducing `Paper Post Editing` time by employing ML-based Automations. 

Data Scientist

Elsevier, Amsterdam
Aug 2018Nov 2022
  • Involved in the development and implementation of automatic document structuring and comprehension techniques, utilizing deep learning models to extract knowledge from lengthy unstructured contexts.  Where, I successfully applied Bi-LSTM+CRFs, BERTs, Longformers, and LayoutLMs to extract entities such as titles, authors, sections, references, and drug names from research articles, improving the efficiency and accuracy of our information retrieval engine.
  • Developed confidence calibrators for deep learning models, enhancing their reliability and interpretability in real-world applications.
  • Led the identification and structuring of references in scientific documents, streamlining the citation process and facilitating easier access to relevant research.
  • Utilized LSTM+CRFs to extract biomedical entities from scientific articles, contributing to advancements in the field of biomedical research and knowledge discovery.
  • Following are some of my successful initiatives: Scientific Document Structuring and Analysis, Extraction of Entities (Title, Author, Scetions, Reference, Drug Name etc) from Research Articles using Bi-LSTM+CRFs, BERTs, Longformers, LayoutLMs, Confidence Calibrators for deep-learning models, Reference Identification and Structuring in Scientific Documents, Extraction of Biomedical Entities from Scientific Articles using LSTMs and CRFs

Research Assistant

University College Dublin
Jan 2018Aug 2018
  • Collaborated with Accenture Labs and University College Dublin on a joint research project aimed at developing automated Anti-Money Laundering Solutions using NLP and deep learning techniques.
  • Applied NLP, NER, Relation Extraction and Knowledge Graphs to enhance the accuracy of AML investigations, reducing human effort and increasing detection rates.
  • Developed a knowledge graph specific to the finance domain, leveraging neural networks and TensorFlow to improve the performance of the automated AML system.
  • Utilized Python and Git for code development and version control, ensuring efficient and organized project management.
  • Presented system demos at conferences, effectively communicating the capabilities and potential impact of the research to industry professionals and stakeholders.
  • Published a paper titled "NextGen AML: Distributed Deep Learning based Language Technologies to Augment Anti Money Laundering Investigation" at ACL 2018, showcasing the advancements made in the field.

Research Associate

Accenture Labs, Dublin
Feb 2017 Dec 2017
  • Developed a decentralized system for NLP and Machine Learning modules, gathering information and intelligence for economic organizations (banks/insurance) to assess the risk and threat associated with clients.
  • Applied NLP, NER, Relation Extraction, Aspect-based Sentiment Analysis, and Knowledge Graph techniques to extract relevant information from financial data and identify entities involved in scams, frauds, and money laundering.
  • Utilized expertise in the finance domain, including KYC and Panama Papers, to enhance the accuracy and effectiveness of clients Anti Money Laundring System.
  • Implemented Neural Nets and Tensorflow for training and deploying machine learning models, achieving high performance and accuracy.  Leveraged Neo4j and RabbitMQ for data storage and message queuing, optimizing system performance and scalability.
  • Collaborated with a team of researchers and engineers across Universities and Accenture lab, Dublin.
  • Developed and filed a patent for an Integrated monitoring and communications system using a knowledge graph-based explanatory equipment management approach.

Research Engineer

Jadavpur University, India
20112013
  • Led the development of a multilingual search engine on the tourism domain, based on Apache Lucene, as part of the Cross Lingual Information Access System (Sandhan Phase - II) project at Jadavpur University.
  • Responsible for generating automatic query-focused summaries and snippets for web pages, developing a query expansion module, and implementing textual entailment-based ranking algorithms.
  • Successfully implemented Java and J2EE technologies, along with Apache Lucene and Nutch, to create a robust and efficient search engine.

Publications(s):