(M.Sc. Artificial Intelligence & Machine Learning@LJMU)
Generative AI Lead | 10+ Years of Experience in AI, NLP, Machine Learning, Big Data
A dynamic professional with over 10 years of cross-cultural experience in Generative AI, Large Language Models, Natural Language Processing, and Big Data. With a strong background in Investment Banking, Finance, and Airlines, I have a proven track record of leading teams and delivering innovative solutions in a global environment.
Cloud & MLOps: Azure AI, Google Vertex AI, AWS Bedrock, AWS Lambda, MLFlow, Kubernetes, LLMOps.
Large Language Models & GenAI: Fine-tuning (LoRA, Prompt Tuning), RAG, VAE, Distributed Inference (vLLM), Distributed Training (Ray), LangGraph, Crew.ai.
Natural Language Processing: Transformers, BERT, FinBERT, Hugging Face, Knowledge Graphs, spaCy, NLTK, Information Extraction, Text Summarization, Q&A.
Machine Learning & Responsible AI: Scikit-learn, FastAPI, Flask, SHAP, LIME, Explainable AI (XAI), Inferential Statistics, Supervised & Unsupervised Learning.
Big Data & Databases: Hadoop, Spark, Kafka, HBase, Cassandra, MS-SQL, Oracle, Data Governance.
Programming & Methodologies: Python, PyTorch, Scala, Java, C#, Agile, Scrum, Test-Driven Development (TDD).
| Certification | Issuing Organization |
|---|---|
| Microsoft Certified: Azure AI Engineer Associate | Microsoft |
| AWS Lambda : Serverless | Amazon Web Services (AWS) |
| Google Vertex AI | Google Cloud |
| Title | Description |
|---|---|
|
Transformer Series Part 1: Encoder Part 2: Encoder-Decoder Part 3: Decoder Only Part 4: Mixture-of-Experts |
A 4-part series on the Transformer Architecture, covering the Encoder, Encoder-Decoder, Decoder-only models, and Mixture of Experts. |
|
GenAI on Prod Part 1: Deploying Part 2: Scaling Part 3: Mastering GPU Efficiency Part 4: Observability |
A series on deploying and scaling Generative AI applications on Kubernetes. |
| LLM Inference (vLLM, TGI, and TensorRT) | An overview of LLM Inference optimization frameworks like vLLM, TGI, and TensorRT. |
| Explainable AI (XAI) | A deep dive into Explainable AI (XAI) methods to make "black-box" models understandable to humans, which is essential for building trust, ensuring fairness, and holding these powerful systems accountable. |
| LLMOps | Architecting Enterprise LLMOps for Scalable, Governed, and Cost-Effective AI. |
| Agentic AI in SaaS | Exploring how Agentic AI is transforming the SaaS landscape. |
| Agentic AI : Dynamic Product Recommendations | Building dynamic product recommendations with LangGraph, featuring a human-in-the-loop and replay workflow. |
| Agentic AI vs AI Agents vs AI Assistants vs RAG | A comparison between Agentic AI, AI Agents, AI Assistants, and RAG. |
| Test-Time Scaling | A look at the rise of test-time scaling techniques, moving beyond "bigger is better". |
| LLM : Distributed Training | A deep dive into distributed training techniques for large language models. |
| Repository | Description |
|---|---|
| LLM Finetuning using LORA | A repository demonstrating how to finetune Large Language Models using the LORA technique. |
| Agents | A collection of examples and implementations of AI agents. |
| NLP with Transformer | A repository with examples of using Transformers for various NLP tasks. |
| NLP Text-Analysis | A collection of tools and examples for text analysis using NLP techniques. |
| AWS AI_SaaS_Lambda | An example of building an AI-powered SaaS application on AWS using Lambda. |
| FastAPI-for-GenAI | An example of building an FastAPI app for GenAI |
I'm always interested in new challenges and collaborations. Feel free to reach out for a conversation.