I am a Machine Learning Engineer with over 3 years of experience working at Tata Consultancy Services (TCS). I have developed and trained advanced models such as ResNet, LSTM, and Graph Neural Networks (GNN) achieving high accuracy levels between 88-92% on projects involving medical imaging and energy forecasting. I have hands-on experience building AI agents, including a LangGraph AI agent that achieved 80% retrieval precision.
My expertise extends to optimizing data pipelines, where I successfully reduced latency by 98% on processing 1.5 million daily records. I am certified in Power BI (PL-300) and currently pursuing a Master of Science in Data Science at the University of Europe for Applied Sciences in Berlin, expected to graduate in 2026.
I am proficient in a wide range of machine learning and deep learning frameworks including PyTorch, TensorFlow, Keras, and Scikit-learn, and have experience with LLM and generative AI tools such as LangChain and FAISS. My skills also cover MLOps practices using Airflow, Docker, and MLflow, as well as advanced SQL and cloud data services like Azure Data Factory.
Throughout my career, I have led teams and delivered impactful projects such as attrition prediction models, anomaly detection systems, and real-time ML pipelines with low latency. I am passionate about applying machine learning to solve real-world problems and continuously improving system performance and reliability.
I am fluent in English and have intermediate proficiency in German (B1 level). I am eager to contribute my skills and knowledge to innovative projects in machine learning and data science.
Built attrition model that flagged at-risk employees 6 weeks early. Deployed anomaly detection on 12+ KPIs, reducing alert time from 2 weeks to 24 hours. Cut ETL latency by 98% on 1.5M daily records via SQL optimization, saving ~500 engineering hours per year. Led a 3-person team shipping 70+ dashboards to CFO/CHRO with zero production incidents across 5 sprints.
Built 8+ dashboards in Power BI and Tableau. Conducted cohort and trend analysis for monthly executive planning.
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