• Driving GenAI enterprise enablement and automation across multiple business units through bot services, data pipeline integration and Agentic AI workflows.

• Enabled DeFi risk analytics and automated Web3 workflows for Coinbase and Kraken, leveraging blockchain infrastructure with Generative and Agentic AI.

• Involved in developing end-to-end machine learning pipelines, fine-tuning Large Language Models (LLMs), and applying GenAI for real-world automation and insight generation.

• Built GenAI-powered APIs integrating OpenAI GPT and HeyGen avatars for personalized customer messaging and interactive support.

• Worked with Chat GPT and Chat GPT API to leverage its capabilities with several applications.

• Prototyped lightweight RAG-like context chaining with BERT embeddings and NER output before full-stack GenAI frameworks became standardized.

• Leading the Generative AI, Agentic AI, Data, Analytics and Digital Transformation portfolio with a global team, driving enterprise AI adoption and supporting strategic initiatives as a Solution Architect/ Engineer alongside the sales & solution team.

• Integrated ChatGPT API into client-facing platforms, automating 86% of manual tasks and reducing support load through contextual AI interactions.

• Integrated Generative AI tools (Hugging Face Transformers, OpenAI APIs) to support demand forecasting and supply chain risk analysis, enhancing decision-making processes with LLM-generated insights.

• Built production-grade, dynamic-response chatbots using the LangChain Framework, leveraging LLMs for real-time, context-aware answers across high-volume environments.

• Developing enablers and accelerator tools using Generative AI, enhancing data pipelines, insights, and analytics to drive better business decisions.

• Developed AI-driven product enhancements as part of broader digital transformation efforts to address customer pain points, resulting in a 30% increase in adoption rates.

ssisting in designing, coding, testing, and debugging software applications under

the guidance of senior developers.

• Supporting the development of reports, dashboards, and forecasting models using

intelligence tools.

• Collaborating with software engineers, product managers, and designers.

• Participating in team meetings, brainstorming sessions, and code reviews.

• Learning to perform unit testing and troubleshoot software issues.

• Documenting technical processes and software functionality for future reference.

• Researching new technologies and assisting with improvements to existing

systems.

• Helping resolve user-reported issues and gathering feedback for enhancements.

• Integrated machine learning models into production environments; deployed models for payroll, risk/fraud, identity verification, and other use cases.

• Developed an AI-powered migration tool that transforms Java Struts applications into FastAPI and React using OpenAI’s real-time API and Hugging Face models.

• Developed LLM-based assistants using GPT-3 (Azure OpenAI) and LangChain, integrated into Microsoft Teams for HR and IT support automation.

• Created FastAPI microservices to serve LLM outputs and integrated them into enterprise dashboards and CRMs.

• Integrated LLM-enhanced workflows into legacy systems using REST APIs and message brokers (Kafka/Azure Queues).

• Delivered GenAI solutions for Fraud/Waste/Abuse, code conversion, image analysis, doc search, hallucination, token optimization, prompt engineering, and fine-tuning.

• Led GenAI solution design to automate campaign targeting, content generation, and funnel tracking for GTM teams, increasing conversion rates by 25%.

• Delivered GenAI solutions with Databricks AI (Mosaic AI, LLMOps, Model Serving) and RAG pipelines for real-time inferencing.

• Delivered AI-powered solutions for fraud detection, predictive analytics, and automation, improving efficiency.

• Developed internal AI guidelines and secured LLM usage for sensitive data prompts.

• Implemented AI-enhanced chatbot workflows for support escalation and ticket classification. Integrated solutions with internal CRM via API and conducted A/B testing to improve customer experience.

• Analyzed AI-generated responses and iteratively improved prompt structures, enhancing model accuracy and reducing hallucinations by 30%.

• Leveraged OpenAI API and Hugging Face Transformers to integrate NLP into reporting pipelines, automating sentiment analysis and summarization tasks for customer feedback.

• Involved in gathering, analyzing and translating business requirements into analytic approaches.

• Implemented MLOps pipelines using MLflow, Docker, Kubernetes, and CI/CD tools (GitHub Actions, Bitbucket, Jenkins) to automate training, deployment, monitoring, and versioning of ML models.

• Developed reusable ML/AI models for forecasting and operational risk mitigation across nodes in the supply chain, deployed via Vertex AI Pipelines and Kubeflow.

• Performed data analysis, visualization, feature extraction, feature selection, feature engineering using Python.

• Applied core NLP techniques—tokenization, stemming, lemmatization, sentiment scoring, and POS tagging— to evaluate rhetorical structure, sentiment intensity, and public reaction trends.

• Developed and tested prompt engineering strategies using various instruction formats (e.g., Q&A, classification, summarization) to assess LLM consistency and hallucination rate.

• Worked on the SAGE project, utilizing RAG (Retrieval-Augmented Generation) Pipeline, LLM Models, GenAI, and Prompt Engineering techniques to enhance the system's capabilities.

• Orchestrated data and ML pipelines using TensorFlow Extended and MLflow, leveraging container registries (ECR, ACR, GCR) for containerization to ensure reproducibility and scalability across cloud environments.

• Containerized ML components using Docker and utilized BigQuery as the data warehouse for seamless data management and integration.