Roles and Responsibilities :

• Lead cross-functional teams of ML engineers and data engineers, implementing Agile methodologies to ensure efficient project management and ML solution delivery.
• Collaborate with product owners, stakeholders, and technical teams to define clear, prioritized backlogs and user stories for ML initiatives.
• Mentor and train junior and mid-level engineers, contributing to their professional growth in machine learning and data engineering.
• Leverage Large Language Models (LLMs) and generative AI to extract insights and enhance content analysis for NBCUniversal's media platforms.
• Lead foundational work on GenAI models in GCP, harnessing LLMs to drive innovations in AI-powered content creation and recommendation systems.
• Design and deploy Agentic AI systems that power intelligent automation solutions across NBCUniversal's digital ecosystem.
• Create feature engineering pipelines for ML models using Python, scikit-learn, and pandas to optimize model performance.
• Manage and optimize enterprise data warehouses and data lakes to facilitate seamless data access for analysts and ML teams.
• Productionise machine learning models in cloud environments including GCP Vertex AI, leveraging Docker and Kubernetes for scalability.
• Implement real-time data streaming solutions using Apache Kafka and Google Pub/Sub, enabling timely data processing for live applications.
• Establish CI/CD pipelines using Concourse and Apache Airflow for automated ML model deployment and monitoring.
• Conduct A/B experiments to measure feature impact and continuously refine models based on experimental results and user feedback.
• Architect scalable ML infrastructure supporting millions of users across NBCUniversal's streaming and digital platforms.

Roles and Responsibilities :

• Led cross-functional teams of Data Scientists, Machine Learning Engineers, and Data engineers to successfully adopt Agile and Scrum methodologies. Ensuring efficient project management and Data Science solutions delivery.
• Facilitated Scrum ceremonies, including sprint planning, daily stand-ups, sprint reviews, and sprint retrospectives, fostering a culture of transparency and continuous improvement.
• Collaborated closely with product owners, stakeholders, and team members to define clear, prioritized backlogs and user stories.
• Mentored and trained junior team members, contributing to their professional growth in data science and machine learning.
• Developed end-to-end model deployment pipelines with ETL data ingestion to the data warehouse and implemented caching strategy techniques to optimize data access and model performance.
• Developed predictive models and machine learning algorithms to solve complex business problems such as behavioral recommendations and drive data-driven decision-making for many clients.
• Developed a knowledge graph of products for the high street bank, which is used to power the company's security network.
• Managed and optimized data warehouses, to facilitate data access for analysts and data scientists.
• Utilized SQL, NoSQL databases, and schema design techniques to ensure data consistency and efficiency.
• Productionized machine learning models in various cloud environments, including GCP Vertex AI and AWS sagemaker, enhancing efficiency through caching strategies Docker and Kubernetes.
• Implemented real-time data streaming solutions using [Specific technologies like Apache Kafka or Pub Sub], enabling timely data processing.
• Implemented CI/CD pipelines using Jenkins and Airflow.
• Conducted A/B experiments to measure the impact of features and refined models based on experiment results.
• Utilized Large Language Models (LLMs) to extract insights and enhance data analysis.
• Authored and published a globally recognised MLOps whitepaper for Accenture.
• Currently engaged in foundational work on GenAI models in AWS, harnessing Large Language Models (LLMs) to drive innovations in AI and ML.

Roles and Responsibilities :

• Developed and monitored deep learning and Machine learning models for financial data in PyCharm or VSCode.
• Developed end-to-end model deployment pipelines for productionising models and rest APIs with best practices.
• Implemented and deployed CI/CD pipelines like Jenkins and MLOps pipelines with airflow.
• Visualised metadata of MLOps and ETL pipeline with Grafana.
• Managed a small team and also performed Scrum Master tasks.
• Requirements gathering from stakeholders and delivering desired results.

Scope of Project

WL - Worldline Germany specialized in the processing of high-volume electronic transactions for principal financial actors on the European market. 3 business lines- Payment Acquiring, Payment Issuing and CRM & e-Services.

1. IFM (Issuer Fraud Management)

IFM is a module which manages the Fraud management for VISA Card or Master Card. The whole process of creating Case for the fraudulent card, Genuine or Fake is handle here. Blocking of Card Checking transactions on Credit Card.

2. AFM (Acquirer Fraud Management)

AFM is a module which manages the Fraud management for Acquirer side or Merchant Side. The whole process of creating Case for the fraudulent transaction, Genuine or Fake is handle here.

3. OLW (Online Watcher)

OLW is a module where rules are defined for the fraudulent cases. It frequently checks for the transaction of cards. If it suspects fraudulent , then automatically creates case and send it to our FCM application.

Roles and Responsibilities :

• Requirement Gathering & Analysis, Development estimation, Development, Documentation, Reviewing & finalizing them with the developers and clients or BU's.
• Development of whole application according to requirements and also checking on various devices, versions, operating systems, and different browsers.
• Making the Hibernate connections for our application, Hibernate Services and DAO class.
• I understood the Banking Domain knowledge. Card schemas like Master Card & VISA Card payment Transactions done using channels like POS terminals, Ecommerce, ATM etc.