Product-focused freelancing forteams that need strong execution.
I help startups and teams ship scalable features, modernize legacy systems, and improve product performance with clear scope and reliable delivery.
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Engagements
Media infrastructure
Video HLS Transcoding Pipeline
An adaptive-bitrate video pipeline: each upload is transcoded with FFmpeg into a six-step ladder from 144p to 1080p and packaged as HLS with a master m3u8 playlist, so players switch quality to match the viewer’s connection.
Six renditions, 144p at 200k up to 1080p at 5000k, each with a matched audio bitrate
Master and per-rendition m3u8 playlists for adaptive playback
BullMQ and Redis queue so transcoding runs off the request path
Dockerized FFmpeg workers for identical output in every environment
FFmpegHLS / m3u8BullMQRedisDockerNode.js
Education platform
Course Cube Learning Management System (LMS)
The backend of a learning platform: a typed Apollo GraphQL API over MongoDB for courses and learners, with BullMQ and Redis running background work so heavy jobs never slow API responses.
GraphQL schema and resolvers for the core learning domain
MongoDB data modelling for content and learner records
Queue-backed background jobs on BullMQ and Redis
Containerized services for consistent deploys
Apollo GraphQLMongoDBBullMQRedisDockerNode.js
Healthcare backend
Aashirvaad Blood Bank Backend
A REST backend for the Aashirvaad blood bank: an Express API on Node.js with MongoDB for donor and stock records, packaged in Docker so it deploys the same way every time.
REST API design around blood bank workflows
MongoDB schemas for donors and blood stock
Validation and error handling on every endpoint
Dockerized deployment
Node.jsExpressMongoDBDocker
AI agent
WhatsApp AI Agent
A conversational agent that runs inside WhatsApp: LangGraph controls the agent’s state and tool calls on top of LangChain, wired to a self-hosted WAHA gateway, with Express receiving messages and MongoDB keeping the conversation history.
Stateful agent flows modelled as a LangGraph graph
Sending and receiving WhatsApp messages through self-hosted, open-source WAHA
Express webhooks that take inbound messages to the agent
MongoDB persistence for conversation history
LangChainLangGraphWAHANode.jsExpressMongoDBDocker
Process
01
Discovery
Map the workload before writing code: the data model, expected traffic and job volume, third-party limits, and how success will be measured.
You get
A scoped brief with an architecture sketch and a milestone plan
02
Execution
Build in small vertical slices, containerized from day one so every increment runs the same locally and in staging. Progress is visible through pull requests and short updates.
You get
Working, reviewable increments in your own repository
03
Validation
Test the hard paths before handover: failed jobs and retries, invalid input, service restarts, and a production-like Docker run.
You get
A tested build and a deployment readiness checklist
04
Handover
Document how it runs and how to change it: environment variables, deploy steps and known limits, then walk your team through it.
You get
Docs, a deploy runbook and a recorded walkthrough option
Engagement Models
Fixed Scope
Milestone-based
Best for well-defined projects
A bounded build agreed up front: one backlog, set milestones, one price.
Single agreed backlog with the scope written down
Delivered in milestones you can review
Clear pricing upfront
Scope changes agreed in writing before work starts
e.g. a video transcoding pipeline or a standalone backend API
Sprint Retainer
Weekly sprints
Best for evolving product roadmaps
Ongoing capacity for a roadmap that keeps changing, planned one sprint at a time.
Weekly sprint goals agreed with you
Priorities can change between sprints
Continuous delivery through pull requests
A short written update at the end of every sprint
e.g. growing an LMS, or adding capabilities to an agent release by release
Tech Partner
Ongoing
Best for founders and lean teams
Senior engineering ownership without a full-time hire.
Architecture and stack decisions
Hands-on implementation, not just advice
Code review and delivery leadership for your team
Roadmap and trade-off guidance
e.g. taking an AI agent product from prototype to production
Planning a build, migration, or delivery sprint?
Share your scope and timelines. I'll reply with a practical execution plan.