Anish Gillella

I am a Data Scientist and AI/ML Engineer with over two years of experience specializing in natural language processing, machine learning, and AI agent development.I have collaborated with early-stage startups to deliver scalable solutions, utilizing my proficiency in Python, C++, JavaScript, and cloud platforms such as Databricks, AWS, Azure, and Google Cloud. My expertise includes training, fine-tuning, and deploying large language models, building MLOps pipelines, and scaling AI/ML applications to support strategic decision-making.

Founding AI Engineer at Airrived Inc. (January – November 2024):

• Developed AI agents for real-time cybersecurity, processing over 50,000 records daily, leading to the successful onboarding of nine initial customers.
• Constructed retrieval-augmented generation (RAG) pipelines using LangChain and Chroma DB, enhancing query handling capacity and accuracy.
• Designed MLOps pipelines with Docker and Kubernetes on Google Cloud Platform, optimizing scaling and reducing deployment latency by 30 minutes per iteration.

• Deployed a RAG system with OpenAI's large language models (LLMs) using Azure Machine Learning Studio, handling over 2,000 queries and improving contextual relevance by 1.5 times.
• Implemented quantization and prompt engineering with LLMs, reducing hallucinations by 40% and response time from 2 to 1.2 seconds for high-volume queries.
• Fine-tuned LLMs using Parameter-Efficient Fine-Tuning (PEFT) methods in Python, boosting predictive accuracy by 12% and reducing training time by 20% across multiple models.

• Collaborated with data scientists to build a recommendation engine using collaborative filtering and deep learning, increasing customer engagement by 30%.
• Optimized data models using SQL queries, enhancing data quality and availability for downstream analytics, reducing report generation time from 5 to 2 hours.
• Built a sentiment analysis application for customer feedback classification, achieving 85% accuracy and increasing customer satisfaction by 20%.

Master of Science in Business Analytics (Data Science Cohort)

• University of Texas at Dallas, Dallas, TX
• Expected Graduation: May 2024
• Focus Areas: Machine learning, data analytics, and AI solution design.
• Relevant Coursework: Advanced Machine Learning, Big Data Analytics, Cloud Computing, and Predictive Modeling.

• Manipal Institute of Technology, Manipal, India
• Graduation: June 2022
• Focus Areas: Software development, data structures, algorithms, and database systems.
• Achievements: Implemented a research project on machine learning algorithms, securing top grades.