Likhith Reddy Bandi
AI Engineer
I'm Likhith Reddy Bandi, AI Engineer at Meta. I specialize in building algorithms for high-precision optical metrology, and I love coding models from scratch to understand them under the hood.”
Skills & Tools
- PythonPython
- LLmsLLms
- RAG ArchitectureRAG Architecture
- Agentic AIAgentic AI
- Prompt EngineerPrompt Engineer
- AWs
- Vector DBVector DB
- PytorchPytorch
- TensorFlowTensorFlow
- Fine TuningFine Tuning
- GCPGCP
- Rest APIRest API
- Fast APIFast API
- MLflowMLflow
- GitGit
- SQLSQL
- RR
- C++C++
- Model EvaluationModel Evaluation
- Model Context ProtocolsModel Context Protocols
- Funtion CallingFuntion Calling
- Data analysisData analysis
- ETL/ELTETL/ELT
- Data visualizationData visualization
- Power BIPower BI
- Data validationData validation
- Machine LearningMachine Learning
- NLPNLP
- Deep LearningDeep Learning
- SnowflakeSnowflake
- predictive modelingpredictive modeling
- dbtdbt
- Langchain and LangraphLangchain and Langraph
- AzureAzure
About Me
AI/ML Engineer driven by the challenge of taking LLMs, AI concepts out of the lab and into production. Specialize in designing scalable AI architecture, optimizing model inference, RAG architecture, multi-agent orchestration, model fine-tuning, building end-to-end production pipelines. Experienced in translating applied AI research into reliable, high-throughput production systems, and boosting inference efficiency at scale.
Strong skills in building scalable data pipelines, ETL/ELT pipelines, data analysis, statistical analysis, data visualization, SQL, Python, and EDA to solve complex business problems. Proficient in managing large-scale datasets, automating reporting workflows, ensuring data quality, identifying trends and delivering actionable insights through data-driven decision-making.
Work Experience
Jan 2025 - Present
AI Engineer
Meta
Designed Meta AI RAG platform using FAISS, Llama, hybrid retrieval, REFRAG-inspired compression, and LLM evaluation, improving latency, retrieval quality, and scalability.
May 2022 – December 2023
Software Engineer
IQVIA
Developed healthcare GenAI applications using RAG, LLMs, Pinecone, AWS, and Vision-Language Models, automating claims review, knowledge retrieval workflows and reduce processing time.
Machine Learning Engineer
MEESHO
Developed recommendation and search systems using XGBoost, NLP embeddings, click, browsing, purchases, and product metadata enhancing improving personalization, product discovery and search relevance.
Education
2024 -2025
Master of Science in Business Analytics and Artificial Intelligence
The University of Texas at Dallas
2019-2023
Bachelors of Technology
National Institute of Technology
Selected Work
RAG
Retrieval-Augmented Generation (RAG) application that combines document management with AI-powered question answering.
Brand Guardian — AI Video Compliance Platform
Deployed Brand Guardian, a multimodal agentic AI platform that reduced manual video compliance review time by 70% via a multimodal agentic pipeline that cross-referenced video content against brand/regulatory guidelines in real time, with full observability stack for production reliability.
SQL Agent
A powerful web application that allows you to upload Excel files and ask natural language questions to generate SQL queries and get data insights. Built with Streamlit and OpenAI's GPT models
Twitter-Emotion-Detection-Analysis
This project focuses on applying natural language processing (NLP) techniques for emotion classification using advanced machine learning models. A variety of models, including LSTM, Transformer-based models (RoBERTa, DistilBERT, ALBERT), QLoRA models (SFR, GEMMA, LLaMA), and fine-tuned transformer models, were implemented and evaluated to identify the most effective approach.
Conversational AI
This is a category-aware Conversational AI chatbot developed for the Jindal School of Management (JSOM) at the University of Texas at Dallas. It allows users to ask natural language questions and receive accurate, relevant answers drawn from structured website content.
Certifications
Machine Learning
Deep Neural network Hyperparameters tuning and optimization
