Full stack development

One engineer from UI to production APIs

Ehsan owns the full delivery path: typed React and Next.js frontends, Node and Python services, PostgreSQL data models, and deployable releases. You get vertical slices you can demo, not handoffs that stall between teams.

Where solutions stay operational

From schema design to guarded LLM workloads, disciplined handoffs teams can staff against.

Full stack deliveryLLM and RAG pipelinesTyped APIs under loadCloud native releasesProduction AI guardrails

Delivery at a glance

How Ehsan ships full stack and AI solutions

Years delivering full stack web systems and production AI features from first schema to monitored release.

12+

Solution areas spanning architecture, typed services, LLM workflows, realtime systems, and cloud delivery.

UI→AI

End to end ownership across interfaces, backend services, data models, integrations, and governed model workloads.

About

Senior Full Stack + AI Engineer

Results-driven Senior Full Stack Web, Mobile, and AI Engineer with expertise in designing scalable web applications, mobile applications, backend systems, cloud-native architectures, microservices, DevOps, and AI-powered platforms. Strong background in SaaS, e-commerce, logistics, healthcare, communication platforms, and AI product development.

Backend ArchitectCloud & DevOps EngineerMobile Application EngineerAI SaaS EngineerReal-Time Systems Engineer

class SeniorFullStackAI:
  def __init__(self):
    self.anchor = "Lahore, Pakistan"
    self.stack = [Node.js, Express.js, NestJS, Python, Django, OpenAI Integration, LLM-based Applications, Multi-Provider AI Integration]

  def ship(self, scope):
    return microservices.llm.ready(scope)

profile = SeniorFullStackAI()
profile.ship(scope="SaaS · commerce · comms platforms")

✦

Selected work

Systems that held up in the real world.

APIs, storefronts, automation, and assistant style features that moved from proposal to monitored production.

Midora AI

Midora AI Multi-Model AI Platform for Content Generation, Chat, and Intelligent Workflows Overview Midora AI is an advanced AI platform designed to unify multiple AI providers into a single system. It enables users to generate content, chat with different AI models, build AI-driven projects, and manage structured knowledge contexts. Instead of relying on a single AI provider, Midora AI integrates multiple models such as OpenAI, Claude, Gemini, and DeepSeek, allowing users to choose or automatically route qu…

One Sale

One Sale B2B Bulk Food Ordering & Agent Management Platform Overview One Sale is a B2B food ordering platform designed for agents who place bulk orders on behalf of their customers. It streamlines wholesale food purchasing, customer management, payment tracking, and order lifecycle handling in a single system. The platform is built for agent-driven commerce, where users act as intermediaries between suppliers and retail shop owners, managing multiple customers and high-volume orders efficiently. Problem Tra…

Capone Food

Capone Food Retail Food Delivery & Bulk Ordering Platform Overview Capone Food is a full-stack retail food delivery platform designed to simplify bulk and individual food ordering for users while giving administrators full control over product management, orders, and customer activity. The platform focuses on providing an efficient browsing and ordering experience across multiple food categories, with support for promotions, wishlist management, and bulk purchasing workflows. It was built to handle both reg…

Ask Me Bot

Ask Me Bot Custom AI Chatbot Platform for Private Knowledge and Embeddable Assistants Overview Ask Me Bot is a customizable AI chatbot platform that enables users and organizations to create intelligent chatbots trained on their own data. It allows users to upload documents, connect external data sources, and build multiple specialized AI bots that can be embedded into any website using a simple script. The platform is designed to transform static knowledge into interactive AI assistants that can answer que…

Workbot

Workbot AI-Powered Enterprise Chatbot Platform for Private Knowledge and Intelligent Assistants Overview Workbot is an AI-powered chatbot platform designed for organizations to create intelligent assistants trained on their private data. It enables businesses to centralize knowledge, connect external data sources, and interact with their information through natural language conversations. Inspired by modern AI systems like ChatGPT, Workbot extends the concept into an enterprise environment where companies c…

Connect

Connect A Real-Time Communication & Collaboration Platform for Teams Overview Connect is a full-stack communication and collaboration platform built to help organizations communicate, collaborate, and manage meetings in one centralized workspace. Inspired by workplace communication tools, the platform combined real-time messaging, video calling, file sharing, team collaboration, and appointment scheduling into a single product. Users could communicate through direct messages, channels, or groups, join video…

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Case studies

Narratives from the hard parts.

Problems, limits, trade offs, and outcomes for leaders who care about maintainability and measurable impact.

Midora AI Case Study # 6: Shared Chat Privacy

How sharing a project chat could expose private context documents through AI responses that quoted or paraphrased them. Fixed with a pre-share similarity scan with user-controlled redaction, a context disclosure notice on all shared project chats so recipients understood the AI had access to private material, and revocable share links with optional expiry dates.

Midora AI Case Study # 5: Abuse Detection

How raw volume thresholds were flagging paying power users while missing sophisticated abusers who distributed their load. Fixed with multi-signal behavioural scoring covering timing regularity, session entropy, and fingerprint diversity, cross-account soft identifier matching for free-tier cycling, and a graduated response of throttle, verify, suspend, deactivate rather than binary blocking.

Midora AI Case Study # 4: Subscription Enforcement

How limit checks run after streaming started were causing responses to cut off mid-sentence with no explanation. Fixed with pre-query token reservation using atomic compare-and-reserve, a credit system with per-model cost weights, progressive usage awareness at 75/90/100 percent, and usage-pattern-based upgrade recommendations.

Midora AI Case Study # 3: Project Context Compression

How raw conversation history hit token limits within weeks and naive truncation was removing the most important early context first. Fixed with a three-tier architecture (permanent core, rolling importance-weighted summary, live window), query-adaptive tier selection that only sent the tiers each query actually needed, and asynchronous summarisation so it never added latency.

Midora AI Case Study # 2: Group Chat Context

How concurrent messages from multiple users were corrupting the shared context and how one talkative participant could fill the context window for everyone else. Fixed with a per-session ordered message queue using atomic sequence numbers, batching of rapid messages into single completion calls, split context budgets per participant, and named attribution in the context so the AI could address people individually.

Midora AI Case Study # 1: Auto Mode Routing

How keyword-based routing was misclassifying mixed queries and adding 800ms+ of visible latency. Fixed with a feature-based lightweight classifier running in under 30ms, parallel model warm-up to hide the classification overhead, a capability-weighted scoring system that balanced complexity and cost, and a user feedback loop feeding weekly retraining.

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Video logs

Notes for teams evaluating AI and platform bets.

𝗦𝗮𝗺𝗲 𝗳𝗲𝗮𝘁𝘂𝗿𝗲. 𝗧𝘄𝗼 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝘀𝘆𝘀𝘁𝗲𝗺𝘀.

The case: "Show a user's transaction history." Team 1 builds a banking app. Team 2 builds a social media app. Same screens. Same API. Same feature. Completely different architecture. That's because of two things every system needs. 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝗮𝗹 𝗿𝗲𝗾𝘂𝗶𝗿𝗲𝗺𝗲𝗻𝘁𝘀 tell you what the system should do. Show the history. Display the numbers. Load the screen. 𝗡𝗼𝗻 𝗳𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝗮𝗹 𝗿𝗲𝗾𝘂𝗶𝗿𝗲𝗺𝗲𝗻𝘁𝘀 tell you how it should behave. How fast. How accurate. How available. For the bank, a sta…

𝗦𝗲𝗺𝗮𝗻𝘁𝗶𝗰 𝘀𝗲𝗮𝗿𝗰𝗵 𝗶𝘀𝗻'𝘁 𝗮𝗹𝘄𝗮𝘆𝘀 𝗲𝗻𝗼𝘂𝗴𝗵.

While working on an AI bot, I noticed that some customer questions were still being missed, even though semantic search was performing well. The solution was surprisingly simple. I combined semantic search with text-based similarity search. Result: • Better answer coverage • Fewer missed queries • More reliable responses • Better customer experience A good reminder that improving AI systems is not always about using bigger models. Sometimes it's about combining the right techniques. 𝘏𝘢𝘷𝘦 𝘺𝘰𝘶 𝘶𝘴𝘦𝘥…

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Validation

Credentials that mirror the work.

Developing LLM Applications with LangChain

Developed advanced expertise in building large language model (LLM) applications using the LangChain ecosystem, focusing on modular AI system design, intelligent orchestration, and production-ready chatbot architectures. Gained hands-on experience integrating OpenAI and Hugging Face models into unified workflows for building scalable, context-aware AI applications. Built practical proficiency in designing and implementing conversational AI systems, including chatbots with structured prompt templates, memory…

Software Engineering Principles in Python

Developed strong software engineering foundations tailored for data science and AI workflows, with a focus on building modular, reusable, and production-quality Python code. Gained practical experience applying core engineering principles to improve code maintainability, scalability, and collaboration in data-driven projects. Built expertise in designing modular architectures that promote code reusability and separation of concerns, enabling more efficient development of analytical and machine learning syst…

Vector Databases for Embeddings with Pinecone

Developed advanced expertise in vector database systems using Pinecone, focusing on scalable storage, retrieval, and management of high-dimensional embeddings for modern AI applications. Gained hands-on experience designing and operating production-grade vector search infrastructure that powers semantic search, recommendation systems, and Retrieval-Augmented Generation (RAG) applications. Built a strong conceptual and practical understanding of core Pinecone architecture, including indexes, pods, projects, …

Introduction to Embeddings with the OpenAI API

Developed advanced expertise in embedding-based AI systems, enabling the transformation of unstructured text into high-dimensional vector representations that capture semantic meaning, context, and intent. Gained hands-on experience building intelligent applications that move beyond keyword matching toward meaning-based understanding of language. Built practical knowledge of generating text embeddings using OpenAI’s embedding models via API integration, and applying these representations to real-world AI us…

Developing AI Systems with the OpenAI API

Developed advanced expertise in building production-grade AI applications using the OpenAI API, with a strong focus on reliability, safety, scalability, and system integration. Gained hands-on experience transforming AI prototypes into robust, production-ready systems suitable for real-world deployment in enterprise and product environments. Built practical knowledge of AI application engineering best practices, including input validation, output moderation, error handling, and safety mechanisms to ensure r…

Unsupervised Learning in Python

Developed advanced expertise in unsupervised machine learning techniques to discover hidden structures, patterns, and relationships within unlabeled datasets. Gained hands-on experience applying clustering, dimensionality reduction, and matrix factorization methods to extract meaningful insights from complex and unstructured data. Built a strong foundation in exploratory machine learning using Scikit-Learn and SciPy, focusing on algorithms that operate without predefined labels or target variables. Learned …

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Proof

What partners highlight.

“I've worked with Ehsan for three years and I have never found a developer partner like him. From my position as Project Manager and System Designer, having him as lead developer made everything easier, crafting high-quality software, solving problems from a different perspective and being able to fill multiple roles when needed; Ehsan is the perfect right hand for every leader and I would like to have the oportunity to work with him once again in the future.”
Cesar Labrador

“Stripe, webhooks, and usage based billing were blocking our go live date. Ehsan implemented payments, idempotent webhook handling, and admin reporting in one pass. Finance finally trusted the numbers and we opened self serve signup on schedule.”
Chris Novak

“I recommend Ehsan to portfolio companies that need a senior engineer who can own architecture and still ship UI. He writes clear specs, flags risk early, and delivers vertical slices you can demo to the board without embarrassment.”
Rebecca Shaw

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