AI/ML Engineer & Solutions Architect | B.Tech CS (AI/ML Specialization) | Final Year @ UPES Dehradun
Passionate about designing intelligent systems that solve real-world problems.
Final-year CS student with deep expertise in machine learning, cloud infrastructure,
and AI solutions architecture. Targeting the DACH region for my career.
- 🧠 Specialization: Machine Learning & AI Systems Architecture
- ☁️ Focus: Google Cloud GenAI, LLM orchestration, production ML pipelines
- 🔍 Currently Building: Multi-agent systems, RAG architectures, LLM benchmarking tools
- 📍 Location: Dehradun, India | 🎯 Target: DACH Region (Germany/Austria/Switzerland)
- 🎓 Expected Graduation: June 2026
- Built and optimized GenAI solutions on Google Cloud Platform
- Hands-on experience with Vertex AI, LLM APIs, and production deployments
- Astronomical Object Detection: Improved IoU from 0.51 → 0.69 through advanced CV techniques
- Expertise in model optimization, evaluation metrics, and performance engineering
- Distributed system for orchestrating multiple LLM agents
- Focus: prompt engineering, agent coordination, output validation
- Tech: Python, LangChain, FastAPI
- End-to-end ML-powered inventory optimization & demand forecasting
- Features: real-time analytics dashboard, predictive restocking
- Impact: Designed for scaling across pharmacy networks
- Production-ready Retrieval-Augmented Generation system
- Comprehensive evaluation framework for RAG quality metrics
- Focus: hallucination detection, relevance scoring, retrieval optimization
- Interactive tool for evaluating and comparing LLM performance
- Metrics: latency, accuracy, cost-per-query, safety alignment
- Use case: Model selection and vendor evaluation
- EcoShare: Sustainable resource-sharing platform
- AI CodeGuard: Code quality and security analysis using ML
Languages: Python, TypeScript, Go, SQL, Bash
AI/ML: PyTorch, TensorFlow, Scikit-learn, Hugging Face, LangChain
Cloud & DevOps: Google Cloud (Vertex AI, BigQuery, Cloud Run), Docker, Kubernetes basics
Databases: PostgreSQL, MongoDB, Vector DBs (Pinecone, Milvus)
Tools: Git, Jupyter, Cursor, VS Code, Linux
30% → Production ML Systems (from notebooks to APIs)
25% → LLM Applications & RAG Architecture
20% → Solutions Architecture & System Design
15% → Cloud Infrastructure & DevOps
10% → Open Source & Community
B.Tech Computer Science (AI & ML Specialization)
Uttarakhand University of Petroleum and Energy Studies (UPES)
CGPA: 7.73 | S7 SGPA: 9.18
Expected: June 2026
- LinkedIn: linkedin.com/in/adrikapradhan
- Email: [your.email@example.com]
- Twitter/X: [@YourHandle]
- 🔬 Building ML models and deploying them to production
- 🤝 Contributing to open-source AI/ML projects
- 📚 Learning in public: sharing insights on solutions architecture, LLMs, and system design
- 🏆 Active on Zindi & ML competition platforms
Seeking MSc in Computer Science / Data Science / AI Engineering programs in DACH region with focus on:
- Advanced ML systems & distributed computing
- Enterprise AI solution architecture
- Cloud-native applications
Post-graduation: Building enterprise AI solutions as a Solutions Architect.
"Good architecture is invisible. Great ML systems feel inevitable."
I believe in building systems that are not just accurate, but scalable, maintainable, and aligned with real-world constraints.
Let's build something intelligent together! 🚀