AI/ML Engineer | MLOps & Data Engineering | Building Scalable, Production-Ready AI Systems

I am a Data Scientist and Machine Learning Engineer specializing in building, automating, and deploying production-ready AI systems. My core focus is bridging the gap between isolated machine learning models and real-world business applications through structured MLOps pipelines, robust APIs, and scalable infrastructure

I engineer end-to-end data workflows—from raw data ingestion to deployment—ensuring that complex datasets translate into reliable, explainable, and actionable insights. Through my professional internships at Deloitte and Zenoti, I developed a strong foundation in scalable data processing, dashboard automation, and delivering stakeholder-focused analytics.

I have architected ML-backed applications across diverse domains, including:

*Predictive Analytics: Customer churn prediction models using XGBoost.
*Financial Technology: Event-based algorithmic trading analytics leveraging FinBERT.
*Supply Chain & Logistics: Geospatial demand forecasting and structured reporting.

My current technical focus includes:

MLOps & Deployment: Docker, MLflow, GitHub Actions
Machine Learning & Data Science: Python, SQL, Pandas, NumPy, scikit-learn, XGBoost
Backend & APIs: FastAPI, REST APIs, schema design, caching
Data Engineering: ETL workflows, batch processing, data validation
Databases & Monitoring: PostgreSQL, model monitoring workflows
Generative AI & NLP: Hugging Face, Transformers, FinBERT, NLP model deployment

I am currently seeking opportunities in Machine Learning Engineering, AI Deployment, Data Engineering, Generative AI Systems, and Applied Data Science.

I am open to connecting with professionals, teams, and organizations working on production-ready AI, data platforms, and intelligent automation systems