Fine-tune Stable Diffusion using LoRA
Learn how to fine-tune Stable Diffusion using LoRA and best practices for hybrid retrieval strategies.
Read MoreBuilding production-grade LLM, RAG, and Voice AI systems. Specialized in ML/GenAI pipelines, low-latency inference, and cloud deployment with hands-on ownership from experimentation to production.
class AIEngineer:
def __init__(self):
self.name = "Parth Panchal"
self.skills = [
"LLM", "RAG", "Voice AI",
"PyTorch", "LangChain",
"Azure", "MLOps"
]
self.analytical_thinker = True
self.startup_ready = True
def build_ai_systems(self):
return "Production Ready"
AI/ML Engineer with around 3+ years of experience building production-grade LLM, RAG, and AI/ML systems. Specialized in ML/GenAI pipelines, low-latency inference, and cloud deployment, with hands-on ownership from experimentation to production in startup environments.
I enjoy working in a role that offers diverse challenges, fosters innovation, and allows me to collaborate on cutting-edge AI/ML projects.I thrive in fast-paced environments where I can build and ship AI products that make a real impact.
STT, TTS, Real-time LLM Integration
LangChain, LangGraph, Prompt Engineering
Azure, AWS, GCP, Docker, MLflow
Docker & Low-latency inference serving.
Production-grade AI/ML projects showcasing expertise in LLMs, RAG, and cloud deployment
Next-gen search engine enabling natural-language product discovery. Architected a hybrid RAG system merging text, vector, and image embeddings.
Agent-based automation for content curation and social media posting. Multi-source aggregation with human-in-the-loop feedback.
End-to-end pipeline for structured data extraction from complex financial PDFs. Led a team of 3-4 engineers managing delivery and client feedback.
Led development of AI system for image-based jewelry description generation. Fine-tuned Stable Diffusion with LoRA for domain-specific image generation.
Built real-time voice AI platform integrating STT, TTS, and LLMs. Reduced end-to-end latency by ~25% with PyTorch optimization.
Developed computer vision model optimized for deployment on NVIDIA Jetson Nano. Edge inference with real-time processing capabilities.
Built automated ML pipeline with data ingestion, training, and deployment. Implemented CI/CD for model versioning and monitoring.
Fine-tuned BERT for multi-class sentiment classification on customer reviews. Deployed as REST API with real-time inference.
Built CNN-based image classifier with transfer learning from ResNet. Achieved 95%+ accuracy on custom dataset.
DeepLearning.AI
CourseraDeepLearning.AI
CourseraIBM
CourseraIntellectual Property India
Journal No. 16/2024Sharing knowledge on AI/ML engineering, best practices, and industry insights
Have an AI/ML project in mind? I'm always open to discussing innovative projects, startup opportunities, and tech collaborations.
India