Head of Delivery
for a Multimodal AI Companion Platform

From MVP to Revenue-Ready Infrastructure

I lead sprint execution, AI system reliability, and stakeholder trust for creator monetization platforms built on LLMs, RAG, and scalable cloud infrastructure.

View Delivery Playbook View Roadmap & Launch Strategy

The Platform

A Telegram-based AI companion system that allows creators to deploy and monetize AI clones of themselves using multimodal interaction, retrieval-augmented generation, and GPU-backed inference.

System Architecture

Creator Dashboard

Web interface for content upload, analytics, and monetization controls

AI Processing Layer

LLM orchestration, RAG pipeline, vector embeddings, GPU inference

Telegram Bot

User-facing conversational interface with multimodal support

Data Infrastructure

Vector DB, Cloud Storage, Monitoring & Cost Controls

LLM Layer

GPT-4, Claude

Vector DB

Pinecone, Qdrant

API Gateway

FastAPI, Express

Serverless

AWS Lambda

GPU Inference

RunPod, Modal

Monitoring

DataDog, Sentry

My Role — Head of Delivery

Sprint Leadership

  • Owned sprint planning and release readiness
  • Ran PR quality gates and feature validation
  • Managed velocity tracking and burndown metrics
  • Controlled scope creep against timeline constraints

AI System Quality Control

  • Validated LLM response quality and hallucination rates
  • Monitored GPU cold starts and inference latency
  • Tracked cost efficiency ($/token, GPU utilization)
  • Established SLAs for availability and response time

Client Stakeholder Management

  • Translated AI limitations into executive-friendly updates
  • Controlled roadmap and scope against revenue priorities
  • Managed pilot user expectations and feedback loops
  • Delivered weekly status reports with risk assessments

Phase-Based Case Study

MVP Scope Matrix

MUST HAVE

  • • Telegram bot with text responses
  • • Basic RAG for creator content
  • • Simple admin dashboard

SHOULD HAVE

  • • Voice message support
  • • Analytics dashboard

LATER

  • • Payment integration
  • • Advanced personalization

Sprint Playbook Preview

  • Week 1 Architecture design & tech stack selection
  • Week 2 Core Telegram bot + LLM integration
  • Week 3 RAG pipeline implementation
  • Week 4 Creator dashboard MVP
  • Week 5 QA, bug fixes, internal demo

Risk Register Snapshot

HIGH

LLM API rate limits during peak usage

MEDIUM

GPU cold start latency >5s

MEDIUM

Vector DB cost scaling unpredictably

LOW

Telegram API policy changes

Delivery Artifacts

Sprint Execution Playbook

5-week sprint framework with quality gates and delivery checklists

MVP Scope Control Doc

Feature prioritization matrix and scope management framework

Client AI Limitations Brief

Executive communication guide for AI system constraints

Quarterly Roadmap

Strategic feature timeline aligned with revenue targets

Production Readiness Checklist

Go-live validation framework with risk mitigation protocols

System Architecture Doc

Technical design decisions and infrastructure scalability plan

AI Systems Leadership

LLM Latency Tradeoffs

Balanced response quality against user experience constraints by implementing a two-tier system: fast GPT-3.5 for simple queries (<1s), GPT-4 for complex conversations (<3s).

Business Impact:

  • • 60% cost reduction on routine interactions
  • • 85% of queries answered in <1.5s
  • • Maintained 4.2/5 quality score

GPU Cold Start Strategy

Solved GPU cold start delays (>8s) impacting user experience by implementing warm pool management and predictive scaling based on traffic patterns.

Solution Architecture:

  • • Maintain 3-5 warm instances during peak hours
  • • Predictive scaling 15min before traffic spikes
  • • Reduced P95 latency from 8.2s to 2.1s

Vector DB Scaling Decisions

Managed vector database growth from 50K to 2M+ embeddings while keeping query latency under 200ms and controlling costs.

Key Decisions:

  • • Implemented hierarchical indexing
  • • Chunking strategy: 512 tokens with 50 overlap
  • • Query cost: $0.002 per search

Cost vs Performance Optimization

Reduced infrastructure costs by 40% while improving system reliability through intelligent caching, rate limiting, and usage-based scaling.

Optimization Framework:

  • • Response caching for 70% of common queries
  • • Dynamic rate limits per user tier
  • • Spot instance usage for batch processing

Incident Simulation

Telegram API rate-limits during a creator launch while GPU latency spikes

24-Hour Response Plan

0-15min

Incident declared, war room initiated, status page updated

15-60min

Implement rate limit backoff, activate backup GPU pools

1-4hr

Monitor recovery metrics, client communication sent

4-24hr

Post-mortem drafted, preventive measures implemented

Client Communication

To: Stakeholders | Status: INVESTIGATING

Subject: Service Degradation - Creator Launch Event

We're experiencing elevated response times (6-8s vs. 2s baseline) due to higher-than-expected traffic during Creator X's launch.

Impact: 15% of users seeing delays, no data loss

Action: Scaling GPU capacity, implementing queue management

ETA: Normal service within 2 hours

Engineering Action Plan

  • Immediate

    Activate 10 additional GPU instances

  • Short-term

    Implement exponential backoff for Telegram API

  • In Progress

    Deploy request queuing with priority levels

  • Long-term

    Auto-scaling triggers based on queue depth

Executive Summary

Incident Duration

2.3 hours

Users Affected

~450 (15%)

Revenue Impact

Low

Resolution Status

Resolved

Root Cause: Underestimated traffic spike combined with Telegram API rate limits

Prevention: Implemented predictive scaling, enhanced monitoring alerts, creator launch playbook updated

About

Technical Program Manager specializing in AI platforms, delivery systems, and operational scaling for LLM-powered products. Focused on predictable execution, cost-efficient architecture, and executive-level stakeholder trust.

I bridge the gap between engineering complexity and business objectives, ensuring AI systems are not just technically impressive but commercially viable and operationally reliable.

5+ Years

Program Management

$2M+

Infrastructure Managed

15+

Product Launches

Discuss Delivery Leadership

Looking for a delivery leader who can scale AI systems from MVP to revenue? Let's talk.