
Anish Gillella
Technology / Internet
Services offered
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.
Experience
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.
Data Scientist at Cohezion.ai (July – November 2023):
- 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.
Machine Learning Engineer Intern at Mentor Juniors and Students Inc. (January – June 2023):
- 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%.
Education
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.
Bachelor of Technology in Computer Science
- 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.
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