AI Enthusiast. Engineer. MBA Finance
π¬Data is how we listen to reality, but data science is how we translate what we hear into something we can understandπ‘
π Get in touch with me π π
| πGitHub | βοΈakash.mathur2289@gmail.com |
| βοΈMedium | π’LinkedIn |
| #οΈβ£Kaggle | π€Space |
ππ¨π½βπ»Generative AI Applications β Project Portfolio π
Senior ML Engineer (Sep 2024 β Present)
60% faster proposal responses by engineering a RAG system with agentic memory, tools, and custom prompts, accelerating RFI evaluations and enabling quicker sales decisions.hybrid search engine with LLM-powered re-ranking, reducing time to insights by 50% and making global data platform more intuitive and user-centric.LLMs to generate task-specific synthetic data using PyTorch, enhancing model performance and reducing bias in AI systems.medallion architecture to build scalable pipelines that cleaned, enriched, and secured unstructured data, making them auditable and AI-ready.Senior ML Engineer β Automotive Supply Chain Technology & Aftermarket (Jun 2021 β Aug 2024)
$500k+ in revenue by architecting end-to-end product on AWS that forecast Vehicles Scrappage. Setup Feature store and model monitoring with SageMaker to track and analyze metrics.deployment and serving with FastAPI through an efficient CI/CD integrated serverless workflow using AWS, Docker, Terraform and GitHub.80% by architecting end-to-end application on AWS. It recommends top news articles through similarity search and summaries generated from a fine-tuned FLAN-T5 model.sentiment analysis using a fine-tuned RoBERTa model and identifying prevalent issues using topic modelling via BERTopic on AWS.Assistant Manager - Quantitative Modelling (Nov 2018 β Jun 2021)
90% by designing ETL pipelines tailored for large-scale data and leveraging ARIMA for revenue forecasting using Databricks.Analyst, Advanced Analytics - Global Markets Technology (May 2015 β Nov 2018)
ETL pipelines, enabling precise identification of high-risk hedge fund investors with the Random Forest classifier.Dimensionality Reduction and Clustering Techniques to identify distinct client groups, leading to deeper customer understanding.| π Technologies | π Interests |
|---|---|
| β’ Languages: Python, SQL, Spark β’ Machine Learning: Scikit-Learn, PyTorch, LangChain, LlamaIndex, Faiss, NumPy, Pandas, MLflow, TensorFlow, NLTK, spaCy, OpenCV β’ Cloud: AWS (SageMaker, Lambda, Batch, Step Functions, ECS, ECR, API Gateway, S3), Azure (Machine Learning, Data Factory, Databricks, DevOps), β’ MLOps: BentoML, MLflow, DVC, Docker, Terraform, CI/CD, Git, GitHub Actions β’ Visualization: Tableau, Power BI β’ Database: MySQL, SQLite, Vector Databases |
β’ Generative AI β’ NLP β’ Recommendation Systems β’ MLOps β’ Stock Market Analysis, Macroeconomics β’ Swimming, Yoga, Meditation, Cooking |
| Post Graduate Diploma in Management - Financial Services | K J Somaiya Institute of Management, Mumbai, India | 2013-2015 |
| Bachelor of Technology - Electronics & Communication | Rajasthan Technical University, India | 2008-2012 |