I build end-to-end AI/ML systems spanning RAG, agentic AI, LLM fine-tuning, and applied deep learning, with a production software engineering background in building scalable APIs, data pipelines, and backend systems.
- Hybrid RAG Suite
- Built a RAG app that ingests PDFs, chunks text, stores embeddings in Chroma, and supports chat over each uploaded document set.
- Implemented hybrid retrieval that combines dense embeddings with BM25, merges ranks with Reciprocal Rank Fusion, and supports optional cross-encoder reranking.
- Built a web Q&A mode that queries Tavily search API, Wikipedia, and arXiv in parallel with timeouts, then generates answers grounded in retrieved sources.
- Created an evaluation module (Hit@K, MRR) with checks via expected-text matches or gold chunk id labels.
- Demo: https://huggingface.co/spaces/ash001/Hybrid_RAG_Suite
- Repo: https://github.com/sparklerz/Hybrid-RAG-Suite
- LangGraph Multi-Agent Orchestrator
- Built a Streamlit multi-agent chat app that routes each query to SQL, Neo4j, web tools, or a general assistant using LangGraph state graphs.
- Implemented a read-only SQLite SQL agent with LangChain tool-calling, capped at 30 steps and 60 sec per run.
- Built a Neo4j agent that generates Cypher from the graph schema, executes it, then answers from query results.
- Integrated web tools and a safe calculator, and logged each tool call.
- Deployed to Hugging Face Spaces using GitHub Actions CI/CD.
- Demo: https://huggingface.co/spaces/ash001/LangGraph_Multi-Agent_Orchestrator
- Repo: https://github.com/sparklerz/LangGraph-Multi-Agent-Orchestrator
- Gemma-2B QLoRA Adapter Fine-Tuning
- Fine-tuned
google/gemma-2bwith QLoRA on Databricks Dolly-15k, using 2,000 train and 200 eval samples. - Trained with 4-bit NF4 (double quant), gradient checkpointing, and 4-step accumulation.
- Trained LoRA adapters with r=8, alpha=16, dropout=0.05 on attention and MLP projection layers.
- Logged metrics to W&B, saved adapter weights with a
run_metadata.json, and published a base vs tuned comparison report. - Adapter: https://huggingface.co/ash001/gemma-2b-dolly-qlora-adapter
- W&B report: https://wandb.ai/kannansarat9/gemma-qlora
- Repo: https://github.com/sparklerz/Gemma-2B-QLoRA-Adapter-Fine-Tuning
- Fine-tuned
- Deep Learning Fundamentals Suite
- Built a multi-page Streamlit app with 6 deep learning demos across tabular data, NLP, time series, and image classification.
- Packaged each model for repeatable inference by saving weights and preprocessing files, then hosting them on Hugging Face Hub for app downloads and caching.
- Trained models in TensorFlow/Keras and PyTorch, including ANNs, SimpleRNNs, LSTMs, and ResNet-18 transfer learning, reaching 97.9% validation accuracy on Cats vs Dogs and 2.86 RMSE on NYC taxi fare.
- Demo: https://deep-learning-fundamentals-suite.streamlit.app/
- Repo: https://github.com/sparklerz/Deep-Learning-Fundamentals-Suite
Before focusing more broadly on AI/ML engineering, I explored distributed and decentralised LLM training and inference.
- Hivemind fine-tuning (Qwen2-0.5B) — Internet-scale data parallelism with DHT + fault tolerance; measured val-loss reductions.
- Petals (LLaMA-2-70B) — Decentralised inference + Deep prompt-tuning via swarm model-parallelism.
- Overview: (meta repo) https://github.com/sparklerz/petals-llama2-70b
- Part 1: https://medium.com/@kannansarat9/part-1-inferencing-llama-2-70b-using-petals-swarm-model-parallelism-over-the-internet-a29de8f8aef3
- Part 2: https://medium.com/@kannansarat9/part-2-prompt-tuning-llama-2-70b-using-petals-model-parallelism-over-the-internet-89cdee667840
- Distributed Multi-GPU LLM Fine-Tuning (monorepo) — PyTorch (DDP, FSDP), DeepSpeed (ZeRO Offload, Pipeline Parallelism), Ray (Train, Tune), MosaicML; W&B/MLflow + HF Hub for fully traceable runs.
Medium: https://medium.com/@kannansarat9
DMs open: https://x.com/saratkannan