Zaid Jamal · ML engineer · Full-stack developer · Founder

Products engineered from idea to scale.

I’m Zaid Jamal, an ML engineer and full-stack developer creating AI, web, mobile, real-time and geospatial systems — from the model to the production system around it.

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The whole product

I work across the entire product.

From product discovery and interface design to backend architecture, cloud infrastructure and production delivery, I build systems as connected products rather than isolated features.

Design

Interfaces, user flows and product decisions designed around real user needs.

Engineer

Scalable frontend, backend, mobile, geospatial and real-time systems.

Deliver

Testing, performance, observability, deployment and long-term maintenance.

Selected work

Products with real users, real constraints and real stakes.

Product systems across mobile, fintech, blockchain, industrial monitoring, generative AI and data engineering — each one owned from architecture to production.

Private beta

LiveCue AI

AI · Desktop · Real-Time Assistance

An AI meeting assistant that provides contextual live guidance while respecting presentation and screen-sharing workflows.

Challenge: Give speakers useful AI guidance during live meetings — transcribing, understanding context and suggesting responses — without ever leaking the assistant into a shared screen or recording.
Contribution: Built the desktop application: live transcription pipeline, contextual AI response generation across local and cloud models, meeting-platform integration and the privacy-aware overlay system.

Desktop app
Live transcription
LLM integration
Local models
Overlay rendering
Meeting platforms

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LiveCue AI end-to-end system flow: audio input, transcription pipeline, context engine, AI generation layer and a private user-only overlay, closed by a continuous feedback loop.

Concept · in development

Phandor

AI Automation · Productivity

An intelligent desktop automation concept designed to help users complete repetitive digital workflows.

Challenge: Let an AI agent operate a user's desktop to finish repetitive workflows — while keeping the human decisively in control of anything consequential.
Contribution: Designed the agent architecture: context understanding, workflow execution engine, human-approval checkpoints and the safety boundaries that decide what the agent may never do alone.

AI agents
Desktop automation
Workflow engines
Context models
Approval systems

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Phandor end-to-end workflow flow: a user request moves through the perception layer, context model and planner to a human approval checkpoint before the execution engine runs the approved step, with every stage writing to an audit trail.

Private build

AI Video Studio

AI · Generative Video · Platform Build

A Higgsfield-style generative video platform turning prompts and stills into cinematic, motion-controlled clips.

Challenge: Serve GPU-heavy diffusion video generation to consumers at tolerable latency and unit cost — while giving them real creative control over camera motion instead of a prompt lottery.
Contribution: Built the platform end to end: Next.js creation studio, Python inference services, GPU worker orchestration with queueing and progress streaming, motion-preset system, moderation pipeline and credits billing.

Next.js
TypeScript
Python
FastAPI
Diffusion models
GPU workers

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AI Video Studio end-to-end creation flow: user input and motion preset selection lead to a low-cost preview job, a moderation gate before GPU spend, the GPU render pipeline, output review and final delivery over CDN.

Delivered

Knowledge Copilot

AI · RAG · Enterprise Knowledge

A retrieval-augmented assistant that answers from an organization's own documents — with citations, not confidence tricks.

Challenge: Company knowledge lives in thousands of documents nobody re-reads. The copilot had to answer from that corpus accurately, cite its sources, respect who is allowed to see what — and refuse when it doesn't actually know.
Contribution: Built the full RAG system: ingestion and chunking pipeline, hybrid retrieval with reranking, permission-aware search, grounded generation with citations, and the evaluation harness that keeps quality honest.

TypeScript
Python
LLM APIs
Embeddings
pgvector
Hybrid search

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Knowledge Copilot grounded answer flow: document sources are parsed, chunked, embedded and indexed, then a user question runs through hybrid retrieval with ACL filters applied before grounded generation returns a cited answer or says it does not know.

Private beta

Autonomous Support Agent

AI · Agents · Customer Automation

A tool-using support agent that resolves real tickets end to end — and knows exactly when to hand off to a human.

Challenge: Support automation usually means deflection. The goal here was resolution: an agent that can look up orders, apply policies and execute real actions — with mechanical limits on what it may do without a human.
Contribution: Designed and built the agent system: the reasoning loop, typed tool layer, policy engine gating consequential actions, escalation logic, and the simulation-based evaluation rig that replayed historical tickets before anything touched production.

TypeScript
Node.js
LLM tool use
Function calling
RAG
PostgreSQL

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Autonomous Support Agent end-to-end flow: a customer message is understood and planned, context gathered, an action decided against policy, permitted tools executed and the result verified — with a human approval checkpoint for consequential actions and escalation when the agent is uncertain.

Prototype · in development

Realtime Voice Agent

AI · Voice · Real-Time

A low-latency voice agent that holds natural phone conversations — booking, qualifying and answering without the robotic pauses.

Challenge: Voice AI lives or dies in the silences. Every stage — hearing, thinking, speaking — adds latency, and anything past a beat feels broken. The agent had to respond inside conversational rhythm and cope with humans interrupting it mid-sentence.
Contribution: Built the streaming voice pipeline end to end: telephony integration, streaming speech recognition, sentence-pipelined LLM and speech synthesis, barge-in handling, mid-call tool use and the latency instrumentation that made every stage accountable.

Python
Telephony / WebRTC
Streaming STT
LLM streaming
TTS
VAD

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Realtime Voice Agent end-to-end conversational flow: an incoming call streams through speech recognition, LLM reasoning, speech synthesis and playback, with mid-call tools and an always-on turn-taking and barge-in controller that can interrupt any stage.

In production

CLZTR

Founder Product · Mobile · Maps · Real-Time

A live city discovery platform connecting people, events and nearby activity through an interactive map.

Challenge: Render thousands of live events, venues and people on a mobile map without melting the frame rate — while presence, clustering and membership state all update in real time.
Contribution: Founded and built the entire product: Expo React Native client, Go services, PostGIS + H3 geospatial layer, Redis and NATS real-time backbone, and the RevenueCat membership architecture.

Expo React Native
TypeScript
MapLibre
PostGIS
H3
Go

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CLZTR end-to-end live map flow: a viewport change resolves to H3 cells, queries PostGIS per cell, merges Redis presence and NATS deltas, and reconciles once per frame into the rendered live map.

Shipped · maintained

CarbonMarketsHQ

FinTech · Carbon Markets · Blockchain · Full-Stack

Financial and carbon-market tools designed around reliable data, valuations and operational workflows.

Challenge: Carbon markets run on fragmented data in mixed currencies and units. The platform had to make valuations trustworthy and workflows auditable for people making real financial decisions.
Contribution: Full-stack engineering across the React/Next.js frontend and Python services: valuation logic, foreign-exchange conversion, transaction workflows, API development and long-term production maintenance.

React
Next.js
TypeScript
Python
PostgreSQL
REST APIs

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CarbonMarketsHQ end-to-end valuation flow: market data is ingested and normalized, a price resolved, FX converted through one audited path, the valuation computed, then moved through transaction workflow into the portfolio view.

Delivered

CAEX Pollution Intelligence

Geospatial · Industrial Monitoring · Real-Time Visualization

A live geospatial monitoring interface for pollution routes, mining vehicles and operational decision-making.

Challenge: Operators needed to see, in real time, where mining trucks were generating pollution along haul routes — across large geospatial datasets that would choke a naive map implementation.
Contribution: Built the real-time visualization layer: moving vehicle rendering, route segments colored by pollution thresholds, time-based filtering and the operational dashboards used for decision-making.

TypeScript
Real-time maps
Geospatial rendering
Time-series filtering
Operational dashboards

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CAEX end-to-end telemetry to decision flow: haul-truck telemetry streams into a bounded vehicle model, joins pre-segmented routes, is evaluated against configurable thresholds and reconciled per frame into a live map and dashboard an operator acts on.

In production

AEMS

Automation · Data Engineering · Event Intelligence

A scalable event acquisition and enrichment system for collecting, validating and publishing structured event data.

Challenge: The public web describes events in a thousand inconsistent ways. AEMS had to turn that noise into a clean, deduplicated, geographically enriched stream of structured events — continuously and unattended.
Contribution: Designed and built the pipeline: Go and Python crawlers, scheduling, validation and deduplication stages, geographic enrichment, and the PostgreSQL/Redis infrastructure that publishes structured events downstream.

Go
Python
PostgreSQL
Redis
Crawlers
Scheduling

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AEMS end-to-end event pipeline flow: acquire, extract, validate, normalize, deduplicate, enrich and publish, with typed rejections feeding a per-source quality score that adjusts crawl cadence.

Delivered

Guardian Methodology Digitization

Blockchain · Carbon Markets · Digital MRV

Digitizing carbon methodologies — VM0048, VM0033 and MECD — into executable, auditable Guardian policies that mint verifiable credits.

Challenge: Translate hundreds of pages of scientific methodology into machine-executable policy without losing scientific intent — then make every claim in the credit lifecycle independently auditable.
Contribution: Led methodology digitization on Hedera Guardian: schema modeling, policy workflow implementation, calculation modules, verifiable-credential chains and token mint integration.

Hedera Guardian
Hedera Hashgraph
TypeScript
Node.js
JSON Schema
W3C DID / VC

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Guardian methodology digitization flow: methodology analysis and schema design become a Guardian policy workflow, where DID-signed submissions feed unit-tested calculation modules, verification anchors to Hedera and verified reductions mint as credits.

Worked with

Brands and teams I've built with.

Engagements across banking, retail, logistics, incident response and AI SaaS — as engineer, consultant or embedded product partner.

All trademarks belong to their respective owners.

Have a product worth building?

Share the problem, current stage and what you need. I’ll respond with practical next steps.

Start a conversation iamzaidbinarshad@gmail.com