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.

• 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

• 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

• 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

• 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

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.