I am a Machine Learning practitioner and Junior Engineer, worked on AI solutions for the US healthcare space.

My current work focuses on building practical AI systems involving LLMs, Agentic AI, RAG, NLP, and intelligent workflows. I enjoy working at the intersection of machine learning, software engineering, and real-world AI applications.

I am particularly interested in building reliable AI systems that combine models, data, retrieval, tools, and orchestration to solve complex problems.

• 🏥 Healthcare AI for real-world healthcare applications
• 🤖 Agentic AI workflows and AI agents
• 🧠 LLM-powered applications using Claude and other foundation models
• 🔎 RAG systems and retrieval pipelines
• 🔗 LangGraph for agent and workflow orchestration
• ⚡ gRPC-based microservices and distributed AI services
• ☁️ Docker & Kubernetes for deploying scalable AI systems
• 🧪 LLM evaluation and AI system reliability

• Machine Learning
• Deep Learning
• Natural Language Processing
• Computer Vision
• Large Language Models
• Generative AI
• Agentic AI
• Retrieval-Augmented Generation
• AI Agents
• Healthcare AI
• Data Science
• Production ML / AI Systems

📫 Reach me: diwash.adhi4@gmail.com

Junior Engineer | Healthcare AI

Working on AI systems for healthcare, with exposure to:

• Agentic AI architectures
• LLM-powered healthcare workflows
• LangGraph
• RAG and retrieval systems
• AI agents and orchestration
• gRPC microservices
• Docker
• Kubernetes
• AI evaluation
• Production AI infrastructure

• Agentic AI workflows
• AI agents
• Multi-agent systems
• LLM orchestration
• Tool calling
• Prompt engineering
• Context management
• Retrieval-Augmented Generation
• LLM evaluation
• AI workflow design
• Claude / Anthropic
• LangGraph

• Supervised Learning
• Unsupervised Learning
• Feature Engineering
• Model Evaluation
• Classification
• Regression
• Deep Learning
• Transfer Learning
• Model Interpretation

• Text preprocessing
• Natural Language Processing
• Text classification
• Embeddings
• Information retrieval
• LLM applications
• RAG

• Image classification
• Image preprocessing
• Transfer learning
• OpenCV
• CNNs
• Model visualization

Currently interested in building production-oriented retrieval and knowledge systems.

• Retrieval-Augmented Generation
• Dense retrieval
• Sparse retrieval
• Hybrid search
• Reranking
• Vector databases
• Knowledge graphs
• Semantic search
• Embeddings
• Context retrieval
• Citation-aware generation

• FastAPI
• LangGraph
• gRPC
• REST APIs
• Microservices
• Docker
• Kubernetes
• Git
• CI/CD
• Distributed AI services

• Cloud-based AI systems
• Containerized applications
• Kubernetes deployments
• Microservice architectures
• Distributed services
• CI/CD workflows

• Git & GitHub
• VS Code
• Claude Code
• AI-assisted development
• Linux / Unix environments
• Shell scripting

AI-powered waste classification system designed for waste segregation.

Technologies:

• Python
• PyTorch
• EfficientNetV2-S
• Computer Vision
• FastAPI
• MongoDB
• Grad-CAM

The system classifies waste into three categories and provides an AI-powered chatbot experience to support waste management.

I have worked on projects involving:

• Machine Learning
• Data Analytics
• Search Analytics
• Content Clustering
• NLP
• Computer Vision
• AI-powered applications
• Data-driven decision making

• Advanced Agentic AI architectures
• LLM application engineering
• RAG systems
• LLM evaluation
• AI agents
• Distributed AI systems
• Kubernetes
• gRPC
• Production ML systems
• Healthcare AI

Building reliable, scalable, and useful AI systems that move beyond prototypes and solve real-world problems.

My long-term interests are at the intersection of:

AI Engineering × Machine Learning × Data Science × LLMs × Healthcare AI