From multi-agent orchestration to production inference pipelines — I design, build, and ship agentic AI that survives contact with real users and real data.
Four technologies. Zero abstraction layers between me and the metal. I architect at the level where frameworks end and real systems begin.
I design stateful agent graphs that coordinate, branch, and recover — not chains that pray. LangGraph gives me explicit control over agent topology, memory persistence, and human-in-the-loop checkpoints. Every node has a purpose. Every edge has a condition.
Prompts are not engineering. They are configuration. DSPy lets me define signatures, compile optimized pipelines, and swap models without rewriting a single prompt. The result: systems that improve themselves through metric-driven optimization, not manual tuning.
The Model Context Protocol is the USB-C of AI tooling. I build MCP servers that give any LLM structured, authenticated access to enterprise data — databases, APIs, internal tools. One protocol. Every model. Zero custom integrations per tool.
Self-hosted models need self-hosted performance. vLLM gives me PagedAttention, continuous batching, and tensor parallelism — the infrastructure layer that turns a GPU cluster into a production-grade inference engine. I deploy models that serve thousands of concurrent requests without breaking a sweat.
Three production systems. Each one solved a problem no off-the-shelf tool could touch.
Multi-agent BI system that turns natural language into verified analytical insights across 4 coordinated AI agents.
AI observability platform that monitors agent reliability, catches drift, and alerts before users notice.
MCP gateway that gives any LLM secure, authenticated access to enterprise tools through one universal protocol.
Building an AI system that needs to actually work?
Let's Architect It TogetherEvery engagement follows the same disciplined arc. No surprises. No scope drift. Just systematic execution.
I map the problem space before I write a single line of code. What does success look like? Where does the current system break? What are the constraints — latency, cost, compliance? This phase produces a clear architecture brief that both of us sign off on.
I design the system on paper first. Agent topology, data flow, model selection, infrastructure requirements, failure modes. You get a technical blueprint and a plain-English summary. No black boxes. Every decision is justified.
Iterative two-week sprints with working demos at each checkpoint. I build with production-grade tooling from day one — not notebooks that need to be rewritten later. You see real progress, not slideware.
Deployment is not the finish line. I instrument everything — traces, metrics, evaluations. The system ships with monitoring built in, so when something drifts, you know before your users do. Then I optimize based on real production data.
I'm Moin Dalal. I architect agentic AI systems for enterprises that need more than a chatbot wrapper around GPT.
My background is unusual for this space: an MBA in Analytics & Strategy, which means I don't just build technically sound systems — I build systems that solve actual business problems. I understand unit economics, stakeholder alignment, and why most AI projects die in the pilot phase.
For the past 12 months, I've gone deep into the agentic AI stack — LangGraph for multi-agent orchestration, DSPy for programmatic prompt architecture, MCP for universal tool integration, and vLLM for production inference. Not surface-level familiarity. Production-grade depth.
I've shipped 3 production systems: Athena (multi-agent BI), Sentinel (AI observability), and Nexus (MCP gateway). Each one solved a problem no off-the-shelf tool could handle.
I work as a solo architect — which means you get one senior person who owns the entire system, not a team of juniors supervised by someone who doesn't write code. Architecture, implementation, deployment, monitoring. One throat to choke.
AI Solutions Architect
Core Stack
Three models. Each scoped to deliver maximum value at the right level of commitment.
A focused engagement to design your AI system architecture. You get a complete technical blueprint — agent topology, model selection, data pipeline design, infrastructure plan, and cost projections. Ideal when you have a clear problem but need an expert to architect the solution before your team builds it.
End-to-end: I architect it, I build it, I ship it. From initial discovery through production deployment and monitoring setup. This is the model for organizations that need a working system, not a document. Includes iterative two-week sprints with working demos at every checkpoint.
Ongoing architectural oversight for teams building AI systems. Weekly 1-on-1s, async architecture reviews, and on-call access for critical decisions. I become your fractional AI architect — embedded enough to add value, independent enough to stay objective.
Ready to scope your project?
Start a ConversationTell me about your system. I'll tell you if I can help — and if I can't, I'll point you to someone who can.
Review within 24 hours
I personally read every message. No assistants, no auto-replies.
30-minute scoping call
We map your system requirements, constraints, and success criteria.
Tailored proposal in 48 hours
Architecture approach, timeline, investment — everything you need to decide.
Typically responds within 4 hours
US & Global remote engagements