Most AI projects don't fail at the idea stage. They fail the first time a real user hits a long-running workflow and nothing comes back. I build the infrastructure that prevents that the layer between an AI demo and a system that handles real users, real data, and real consequences.

In 2026, prompting is a commodity. The engineers worth hiring are the ones who keep an agent running under real load, recover it when it crashes, and know exactly what it was doing when it did. That is the work I focus on.

Production-grade AI expertise
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Durable agentic workflows. I architect multi-agent systems using LangGraph and Temporal. At Neptune Technologies, I built Temporal-backed pipelines that maintained 99%+ data completeness across thousands of long-running voice calls — workflows that survived crashes, handled retries, and held state without anyone watching them. Long-running tasks don't vanish. They finish.

Voice AI that doesn't feel like a bot. I build low-latency voice pipelines using LiveKit and WebRTC, with Deepgram and ElevenLabs in the stack. The engineering target is always the same: reduce the perception of lag until the conversation feels real. I've done this in production for fintech loan disbursement verification — a domain where the call has to work, legally and technically.

Enterprise context via MCP. I implement Model Context Protocol servers that give agents secure, structured access to your CRMs, SQL databases, and internal tools. At Neptune, this unified context across 20+ SaaS tools on a single orchestration layer running five LLMs simultaneously — and cut inference latency by 30% through custom caching and optimized async I/O.

System observability from day one. Every system I ship is instrumented before it goes live — Langfuse for agent reasoning and cost, Prometheus for infrastructure. If something breaks in production, you will know what the agent was doing and why before you even ask.

Delivery & track record
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I have moved systems from Python prototype to containerized, production-grade service. Earlier in my career I built backend automation at Cloud4C handling 7,000+ daily events at 99.9% fault tolerance, cutting manual ticket triage by 75%. The discipline from that work — idempotent services, proper error handling, event-driven architecture — carries into every AI system I build now.

One client kept me embedded in their ML development team for nine months. Ten independent clients used the same word to describe the engagement: quality. I mention this not as a credential but because it describes how I actually operate — and because the clients worth working with tend to notice the difference.

Technical ecosystem
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AI & Orchestration — LangGraph, LangChain, CrewAI, Autogen. Temporal (Workflows, Activities, Signals, Queries). Ragas and Langfuse for evaluation. GPT-5, Claude 4, Llama 4, Ollama, Groq.

Voice & Real-Time — LiveKit, WebRTC, SIP, RTP, Deepgram, ElevenLabs, OpenAI Realtime API.

Backend & Infrastructure — Python (FastAPI, Pydantic), Go. Docker, Kubernetes, AWS, Azure, GitHub Actions. Apache Kafka, RabbitMQ, Redis Streams. PostgreSQL, MongoDB, Redis. Pinecone, Weaviate, Milvus.

What you can expect
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Systems-first delivery. Not scripts that work once in isolation. Services with logging, error handling, and observability built in from the start — because retrofitting these into a broken production system costs three times what it would have taken to include them.

Technical honesty. If your use case doesn't need a large language model, I'll tell you. If a specific model is overkill for your budget and use case, I'll say so. I have lost projects being upfront about this. The ones I keep tend to run long.

Clean handoff. Containerized, fully documented codebases — built for your team to maintain, extend, and understand without me in the room.

If your AI project needs to hold up past the demo, reach out.