Need AI that actually works in production? I build ML systems that scale from model training to deployment pipelines. PyTorch, TensorFlow, LLM integration with LangChain. Your AI problem, solved and deployed.
Need AI that actually works in production? I build ML systems that scale from model training to deployment pipelines. PyTorch, TensorFlow, LLM integration with LangChain. Your AI problem, solved and deployed.
Full AI stack
From models to deployment
Production-ready
MLOps and scalable infrastructure
Production-ready AI solutions with optimized models, MLOps pipelines, and scalable infrastructure.
Clear outcomes, the right guardrails, and async updates while we work.
Availability: 1–2 concurrent builds max.
Timeframe: Typical engagement 6–10 weeks.
Collaboration: Weekly demos, shared roadmap, <24h async response.
How we break down the work so you stay unblocked at every phase.
Built a Retrieval-Augmented Generation system processing 10M+ documents with semantic search, vector embeddings, and real-time query optimization. Deployed LangChain agents with function calling and memory.
Developed a computer vision system using YOLO and Transformer models for real-time object detection and tracking. Deployed on edge devices with TensorRT optimization achieving 60 FPS.
Built scalable MLOps infrastructure with automated training pipelines, model versioning, A/B testing, and monitoring. Includes feature stores, experiment tracking, and automated retraining.
Fine-tuned GPT and Llama models on domain-specific data with LoRA and QLoRA techniques. Implemented prompt engineering, chain-of-thought reasoning, and evaluation frameworks.
Developed predictive models using LSTM, Transformer, and ensemble methods for financial forecasting. Achieved 92% accuracy with real-time inference and continuous learning pipelines.
Founders and operators keeping us honest.
Built our LLM integration—handles 10K requests/day.
Shubham integrated LLMs into our product with RAG architecture. The system handles 10K requests per day seamlessly. He set up proper error handling, rate limiting, and monitoring. Production-ready from day one.
MLOps pipeline reduced our model deployment time by 80%.
We were manually deploying ML models. Shubham built an MLOps pipeline with automated training, versioning, and deployment. Model deployment time went from days to hours. The monitoring and alerting he added caught issues early.
Fine-tuned our LLM model—accuracy improved 25%.
Shubham fine-tuned our LLM model for our specific use case. Accuracy improved by 25%, and inference time stayed the same. He also built the API and monitoring infrastructure. Great work.
I specialize in machine learning, deep learning, LLM integration, computer vision, and MLOps. I work with TensorFlow, PyTorch, FastAPI, Django, and production deployment pipelines.
I offer AI model development, LLM integration, computer vision solutions, MLOps pipeline setup, and AI consulting. Available for contract and hourly work. Contact me to discuss your AI project needs.
I start by understanding your AI requirements and data, then design and develop models tailored to your needs. I focus on production-ready solutions with proper MLOps pipelines, monitoring, and documentation.
I have experience deploying AI models to production using FastAPI, Django, Kubernetes, AWS SageMaker, and MLflow. I ensure models are scalable, monitored, and maintainable in production environments.