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Anish Gillella

Anish Gillella

AI/LLM Engineer

Technology / Internet

Dallas, Dallas

Social


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.

Approximate rate: USD$ 40 per hour

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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