I’m a Senior Machine Learning & Distributed Systems Engineer with 10+ years of experience architecting large-scale AI infrastructure, GPU inference platforms, and multi-agent (MCP) systems. I specialize in mission-critical systems demanding tight control over latency, reliability, orchestration, and control-plane design.

I've shipped:

• Real-time distributed systems at Microsoft
• Identity & risk-scoring engines at AWS
• Multi-agent (MCP) platforms for automating developer workflows end-to-end

• Designed real-time platforms processing 10M+ GPU telemetry events/day (TP99 <120ms, 99.99% uptime, Azure-scale compute)
• Architected global, multi-region orchestration systems using Kubernetes, Synapse, Spark, ADF (2s pipeline latency reduction)
• Built low-level telemetry & diagnostics for Maia 100 AI accelerators (Redfish API integration)

• Developed multiple MCP servers powering:
  • Automated PR generation
  • Repo-wide code intelligence
  • Contextual retrieval from CI/CD + logs
  • Issue tracking & GitHub tool integration
• Implemented deterministic workflows, tool-calling chains, and developer automation pipelines
• Designed Mosaic-style agent frameworks (planning, reasoning, orchestration)

• Created end-to-end log-based RAG for diagnostics/investigation workflows
• Built a full restaurant recommendation RAG system with LlamaIndex + Elasticsearch (vector search, hybrid retrieval, embeddings, caching)
• Developed semantic search tools for design docs (Azure AI Foundry + Semantic Kernel). Improved retrieval efficiency by 60%

• Architected microservice-based ML pipelines and anomaly detection frameworks
• Built scalable ETL pipelines (Spark, DynamoDB, Kafka), integrated distributed monitoring/alerting
• Designed REST APIs, CI/CD workflows, and containerized services for cloud platforms

Infrastructure & Distributed Systems

• Kubernetes • Docker • Spark/Flink • Synapse • Redfish API
• Control Plane Design • Autoscaling • Routing

AI / ML / LLM Systems

• Inference Pipelines • Vector Search • RAG • Embeddings • Observability
• Feature Engineering • GPU Telemetry

Agents & MCP

• Tool Calling • Multi-agent Orchestration • PR/Repo Automation • Deterministic Workflows • MCP Servers

Cloud Platforms

• Azure (AI Foundry, Functions, Compute, AI Search)
• AWS (SageMaker, DynamoDB, CloudFormation)
• GCP (Familiar)

Languages

• Python • Go • Java • C++ • Bash • JavaScript/Node • SQL/NoSQL

A complete LlamaIndex + Elasticsearch based system utilizing multi-source ingestion, hybrid retrieval, embeddings, and chat-style personalization.

Multi-agent workflow automation for code review, PR generation, CI/CD understanding, and intelligent repo analysis.

Replicated Databricks Agent Bricks patterns: tool orchestration, structured reasoning, vector-based retrieval, agent messaging layers.

• Building distributed inference & scheduling systems
• Designing latency-aware routing, capacity planning, and control-plane components
• Creating MCP-enabled agent ecosystems for automation & reasoning
• Optimizing GPU utilization and system reliability at scale
• Scaling observability, health monitoring, and model versioning
• Architecting backend systems for mission-critical AI workloads

• Advancing agent orchestration with MCP
• Building deterministically reproducible agent workflows
• Improving inference through caching, batching, and routing
• Developing RAG systems grounded in operational logs & telemetry
• Exploring LLM safety, validation, and structured reasoning integrations

If you’re working on high-performance AI infrastructure, next-gen inference, or agentic frameworks, let’s connect!
I'm especially interested in collaborations where safety, reliability, and real-time performance are paramount.