JEEVAKAMAL K RAI Engineer.
I build and deploy production-grade intelligent systems. Specializing in scalable architectures, hybrid RAG pipelines, and edge optimization.
Industry Experience
AI Trainee(Product Development)
UVS Cube Infotech
Mar 2026 – May 2026 | Madurai
Designed and deployed production-grade healthcare AI systems with deterministic safety guarantees and low-latency hybrid retrieval pipelines.
Medical Triage Chatbot
The Architecture
Built a hybrid RAG pipeline combining FAISS (dense) and BM25 (sparse) retrieval over PubMedBERT embeddings. Integrated with FHIR R4/ABDM for real-time patient history injection into LLM prompts.
Engineering Metrics
- Engineered a deterministic severity scoring engine (0–100) with red-flag detection.
- Implemented Triple-Gate output safety guaranteeing zero false diagnostic statements.
Voice Assistant Pipeline
The Architecture
Developed an end-to-end voice pipeline using Whisper for ASR, Llama-3.1 for NLU, and pyttsx3 for TTS. Enabled voice-to-UI control for hands-free healthcare app navigation.
Deployment & Infrastructure
- Deployed both systems to production via Docker + NGINX.
- Automated deployment pipelines using GitHub Actions CI/CD.
- Implemented Redis session caching and Groq→Gemini LLM failover strategies.
Advanced Architectures
Production-grade model architectures and cyber-physical systems.
Cyber-Physical System
Digital Twin for Urban Microgrid Resilience
Designed a cyber-physical digital twin integrating CNN-BiLSTM load forecasting, Model Predictive Control (MPC) for dispatch optimization, and Extended Kalman Filter (EKF) for real-time state estimation.
Load Forecasting
CNN-BiLSTM with 96% R²
Control System
MPC + EKF real-time loop
Communication
MQTT pub/sub pipeline
Decentralized Architecture
FedRetinaNet: Defect Detection
Developed a privacy-preserving federated RetinaNet framework for insulator defect detection. Achieved mAP@50 of 0.91, retaining 97% of centralized performance while preserving edge data privacy.
Physics-Constrained ML
EV Range Prediction using PINNs
Engineered a Physics-Informed Neural Network (PINN) layered with LSTM and Attention mechanisms to predict EV range under non-stationary real-world traffic with SUMO simulation.
Affective Visual Intelligence
AVIS: Emotion Recognition & Image Captioning
Built a dual-stream Affective Visual Intelligence System combining EfficientNet + MediaPipe CNN for 7-class emotion recognition (63% accuracy) with BLIP and LangGraph AI for empathetic captioning.
Applied AI Projects
Agentic Knowledge Graph
ResearchOS: Enterprise GraphRAG Platform
Built an autonomous AI research operating system using Multimodal RAG and LangGraph agents. Engineered a tri-service microservice architecture (Nginx, FastAPI, Neo4j) to extract entities from ArXiv papers and construct explicit, traversable Knowledge Graphs for hallucination-free reasoning.
Model Engineering & Optimization
Efficient English-Tamil Translation
Fine-tuned a LLaMA-3.1-8B model specifically for English-to-Tamil translation. Utilized 4-bit LoRA (Low-Rank Adaptation) and Unsloth to drastically reduce memory footprint and training time while maintaining high BLEU scores.
Journal Publications
Neural Computing and Applications (Springer)
August 2026
Hybrid Attention-Enhanced Physics-Informed Neural Framework for Accurate Electric Vehicle Range Prediction
Presented a novel approach utilizing Physics-Informed Neural Networks (PINNs) to predict electric vehicle range. The model integrates physical laws, such as energy conservation and battery dynamics, with neural network training to ensure physically consistent and highly accurate predictions, outperforming traditional data-driven baselines.
Research Works
HPC & Edge Deployment
Heterogeneous Satellite Imaging Benchmark
Developed a comprehensive benchmark for heterogeneous satellite imaging systems, integrating High Performance Computing (HPC) with Computer Vision (CV) to optimize edge deployment and processing performance.
Computer Vision & IoT
Shelf-Life Prediction of Fruits
Designed an automated shelf-life prediction system for fruits using advanced Deep Learning models deployed alongside IoT sensor networks for real-time environmental monitoring and visual analysis.
Technical Expertise
AI Systems Engineer — Bridging AI & Systems
Specializing in building end-to-end AI pipelines, from training custom architectures and fine-tuning large language models to deploying robust, high-performance backends for mission-critical and domain-specific applications.
