Machine Learning Engineer with 5+ years of experience delivering scalable AI/ML solutions deployed in production across financial and healthcare domains. Specialized in Generative AI (LLMs, RAG, Prompt Engineering, Agentic AI systems), Deep Learning, and Classic ML. Developed 5+ end-to-end ML pipelines and production LLM systems using Python and Hugging Face, improving automation efficiency by 40%. Skilled in data pre-processing, model optimization, and explainable AI (XAI) techniques including SHAP and LIME. Integrated AWS/GCP cloud infrastructure, containerized deployments using Docker/Kubernetes, and enabled real-time inference pipelines for production environments. Experienced in mentoring students and leading agile teams that deployed 4 scalable ML models across research and industry, reducing time-to-insight by 40%.
Machine Learning Engineer with 5+ years of experience delivering scalable AI/ML solutions deployed in production across financial and healthcare domains. Specialized in Generative AI (LLMs, RAG, Prompt Engineering, Agentic AI systems), Deep Learning, and Classic ML. Developed 5+ end-to-end ML pipelines and production LLM systems using Python and Hugging Face, improving automation efficiency by 40%. Skilled in data pre-processing, model optimization, and explainable AI (XAI) techniques including SHAP and LIME. Integrated AWS/GCP cloud infrastructure, containerized deployments using Docker/Kubernetes, and enabled real-time inference pipelines for production environments. Experienced in mentoring students and leading agile teams that deployed 4 scalable ML models across research and industry, reducing time-to-insight by 40%.
- Led hands-on sessions on transformers, prompt engineering, and RAG pipelines using OpenAI, Hugging Face, and LangChain,
- improved student task accuracy by 35% and course engagement by 40%.
- Mentored 15 capstone teams on LLM fine-tuning and deployment strategies, increased project completion rates by 30% and
- publication quality by 25%.
- Spearheaded migration from monolithic to microservices architecture and integrated machine learning–based fraud detection,
- minimizing fraudulent activity by 25%. Championed an Agile transformation, improving team velocity and shortening release cycles.
- Orchestrated ETL pipelines by using Python, SQL, Airflow processing 1M+ daily records, boosting data throughput by 30% and
- doubling analytics speed.
- Constructed claims automation system by integrating NLP chatbots, leading to a 35% increase in claims processed per employee,
- solidifying the department as the top-performing vertical in the company.
- Deployed and monitored TensorFlow models with real-time logging, achieving 98% uptime and improving model reliability by 20%.
- Designed and launched real-time recommendation APIs (Flask, TensorFlow), boosting user engagement by 20%.
- Crafted interactive analytics dashboards (Tableau, Power BI) for three business units, accelerating reporting by 25%.
- Streamlined MongoDB NoSQL performance for high-frequency tracking, cutting query latency by 40% and scaling capacity.
- Engineered and implemented Python data cleaning pipelines with Pandas and NumPy, processing 1 million+ records monthly,
- improving data quality scores by 40% and halving manual effort.
- Led ML proof-of-concepts in fraud detection and customer segmentation, boosting anomaly detection accuracy by 22%.
- Created internal dashboards, enabling three departments to accelerate decision-making and reduce report turnaround by 25%.