I'm a Senior AI Engineer, Bittensor Subnet Developer, Miner Operator, and Fullstack AI Systems Builder with 10+ years of experience designing, deploying, and scaling intelligent software systems.
My work focuses on the intersection of:
- Decentralized AI
- Bittensor subnets
- Miner and validator architecture
- Agentic AI
- LLMs and RAG
- Production ML systems
- High-performance inference infrastructure
I enjoy building AI systems that are not only smart, but also useful, scalable, measurable, and economically aligned.
Bittensor is a decentralized machine intelligence network where specialized subnets coordinate miners and validators around useful AI tasks. I focus on building subnet systems where model performance, incentive design, infrastructure reliability, and real-world utility all work together.
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Subnet Development
- Designing custom subnet architectures
- Implementing miner / validator protocols
- Building task-specific scoring and reward mechanisms
- Developing incentive models for useful AI behavior
- Working with Subtensor, metagraphs, wallets, hotkeys, coldkeys, and subnet registration flows
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Miner Engineering
- Building high-performance miners for AI workloads
- Optimizing inference latency, throughput, and reliability
- Deploying GPU-backed miner infrastructure
- Monitoring miner uptime, emissions, scoring, and competition performance
- Hardening miners for long-running production environments
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Validator & Scoring Systems
- Creating robust validation pipelines
- Designing evaluation logic for miner responses
- Implementing anti-gaming and quality-control mechanisms
- Benchmarking miners using task-specific metrics
- Building validator observability and analytics dashboards
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AI-Native Subnet Use Cases
- LLM inference subnets
- RAG and knowledge retrieval subnets
- Agentic reasoning subnets
- Data intelligence and analytics subnets
- Embedding, ranking, classification, and model-serving subnets
- Autonomous AI agents capable of multi-step reasoning
- Tool-using agents for search, retrieval, code, data, and APIs
- LangGraph-based workflow orchestration
- Planning, reflection, routing, and memory systems
- Root-cause analysis agents for technical and business workflows
- Production-grade RAG systems
- Hybrid retrieval using semantic search + keyword search
- Re-ranking pipelines for stronger answer relevance
- Natural-language-to-SQL agents
- Domain-specific LLM applications
- Prompt engineering, evaluation, and model optimization
- Internal AI knowledge engines
- Vector search and metadata filtering
- Document ingestion and chunking pipelines
- Embedding infrastructure
- RAG evaluation frameworks
- Human-in-the-loop feedback systems
- End-to-end ML pipeline development
- Real-time inference APIs
- Recommendation systems
- Model evaluation and monitoring
- Data preprocessing and feature engineering
- Scalable deployment with Docker and cloud infrastructure
- Subnet protocol design
- Miner implementation
- Validator implementation
- Reward function engineering
- Synthetic task generation
- Scoring and benchmarking
- Metagraph monitoring
- Wallet / hotkey / coldkey operations
- GPU miner deployment
- Dockerized subnet infrastructure
- Logging, metrics, and observability
- Latency and throughput optimization
- Building Bittensor miners with stronger inference performance
- Designing custom subnet incentive mechanisms
- Developing validator scoring pipelines
- Creating agentic AI systems that can reason, retrieve, and act
- Optimizing LLM inference latency and cost
- Building RAG pipelines with hybrid retrieval and re-ranking
- Deploying GPU-backed AI services in production
- Exploring decentralized markets for machine intelligence
| Area | What I Build | Core Skills |
|---|---|---|
| Bittensor Subnets | Miner / validator protocols, reward logic, scoring systems | Python, Bittensor, Subtensor, Docker |
| AI Miners | GPU inference miners, monitoring, optimization | PyTorch, CUDA, FastAPI, Linux |
| Agentic AI | Autonomous workflows and tool-using agents | LangGraph, LangChain, LLM APIs |
| Enterprise RAG | Knowledge engines and retrieval systems | Qdrant, PostgreSQL, embeddings |
| Production ML | End-to-end ML systems and inference APIs | MLOps, FastAPI, Docker, cloud |
| Fullstack AI Apps | AI-powered dashboards and platforms | Next.js, React, TypeScript, APIs |
I believe the next generation of AI systems will be:
- Open
- Decentralized
- Measurable
- Incentive-aligned
- Composable
- Useful in production
That is why I’m especially excited about Bittensor, where intelligence becomes a competitive, decentralized, and permissionless network.
My goal is to build AI systems that perform well in benchmarks, survive real-world usage, and create value for both users and network participants.