Freelancers using Pipeliner

Freelancers using PipelinerFreelancers using Pipeliner

Projects People

Siddiq Tashfeen

proSt. John's, Canada

Full-Stack Engineer | AI Automation & Workflow Systems

28: Followers

Full-Stack Engineer | AI Automation & Workflow Systems

Cover image for Real-Time Product Analytics Dashboard with

Real-Time Product Analytics Dashboard with Feature Flags & Event Tracking Enabled real-time monitoring of 7M+ page views and user sessions to support data-driven product decisions.

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Cover image for Outbound Lead Generation & Automation

Outbound Lead Generation & Automation System (Email + LinkedIn) I built a complete outbound system to generate and convert high-quality B2B leads using email and LinkedIn automation. The system starts with ICP-based lead sourcing, targeting companies by size, revenue, industry, and C-level roles. Leads are enriched, segmented, and automatically routed into a structured outreach flow. I designed a 6-step email sequence where messaging evolves at each step to increase reply rates. LinkedIn actions are timed between email touches to create a natural multi-channel outreach without overlap. The main challenge was coordinating multiple tools while maintaining deliverability and clean data flow. I solved this by building a centralized automation layer that handles sequencing logic, delays, and real-time updates across systems. A lightweight CRM tracks every lead from first contact to final outcome, including replied, interested, and not interested. This provides clear visibility and allows continuous optimization. The system is structured for scalability and simple handoff, so it can be managed without technical dependency.

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Cover image for Built a feedback and workflow

Built a feedback and workflow management system focused on collecting, organizing, and prioritizing user-driven inputs across multiple channels. The platform provides a structured interface where feedback items move through defined stages, enabling teams to track status, engagement, and impact in a clear and scalable way. On the backend, I designed data models and filtering logic to support advanced querying, segmentation, and lifecycle management of feedback items. Implemented role-based access control and admin-level configurations to manage permissions, visibility, and workflow transitions. This ensures that internal teams can efficiently manage large volumes of data while maintaining consistency and control. On the frontend, I focused on building clean dashboards and interactive views that surface key metrics such as user reach, engagement, and revenue impact. I also implemented public-facing and read-only views, allowing external users to submit feedback and track updates without direct system access. This improves transparency while keeping the system secure and well-structured.

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Cover image for I built a workflow management

I built a workflow management system focused on tracking projects from start to completion with clear visibility and control at every stage. The core of the system is a multi-role dashboard that presents real-time data in a structured way, allowing different users to quickly understand project status, performance metrics, and operational health without digging through raw data. On the backend side, I designed structured data models and lifecycle flows to manage how tasks move through different stages (creation → in-progress → completed). I implemented role-based access control and admin-managed configurations so the system can handle multiple users, permissions, and dynamic updates reliably. The goal was to make the system scalable and maintainable while keeping the logic clean and predictable. For the frontend, I focused on building intuitive dashboards and reporting layers that translate complex data into simple, actionable insights. I also implemented shareable, read-only views so external users can track progress through a secure link without needing full access. This improves transparency while maintaining control over the system, which is critical for B2B workflow tools.

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Charlottetown, Canada

Senior Full Stack Engineer | Web, Cloud & Automation

8: Followers

Senior Full Stack Engineer | Web, Cloud & Automation

Cover image for AI Knowledge Intelligence Platform (RAG

AI Knowledge Intelligence Platform (RAG System) Built an AI-powered knowledge assistant that retrieves and synthesizes information from internal documentation using natural language queries.

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Cover image for AI Phone Agent Platform: Automate Call Handling & CRM Sync

AI Phone Agent Platform: Automate Call Handling & CRM Sync

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Cover image for Kliks – Mileage & Reimbursement

Kliks – Mileage & Reimbursement Mobile App (React Native) About Project Kliks Mileage & Reimbursement is a high-performance mobile application built with React Native, designed to streamline mileage tracking and expense reimbursements for enterprise users. I led the frontend development and played a key role in transforming a legacy, poorly optimized codebase into a scalable, performant, and design-consistent mobile application. Key Contributions Pixel-Perfect UI Implementation from Design Specs Translated design requirements into pixel-accurate React Native screens with precise spacing, typography, and layout consistency Standardized UI patterns across the app using reusable components and consistent styling Replaced inconsistent UI elements with structured, accessible components aligned with a unified design system

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Cover image for Procedural particle simulation creating creature-like

Procedural particle simulation creating creature-like formations through emergent behavior. Built using generative algorithms and real-time rendering. Skills: creative coding, particle systems, mathematical modeling. Tools: JavaScript, WebGL, Three.js, GLSL.

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Moiz Ahmed Mansoori

Karachi, Pakistan

AI Assistants, RAG Chatbots & Business Workflow Automation

New to Contra

AI Assistants, RAG Chatbots & Business Workflow Automation

Cover image for Built a full-stack Todo app

Built a full-stack Todo app across 5 phases — CLI to cloud-native. Phase 3 adds AI chatbot via LangChain and Groq for natural language task management. Phase 4 containerized with Docker and Kubernetes. Phase 5 deployed on Vercel with Neon PostgreSQL. 4 live deployments.

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Cover image for QueryMind lets you query a

QueryMind lets you query a real database using plain English. Type a question, get an answer — no SQL knowledge needed. Built a production-grade AI agent using LangGraph that converts natural language to SQL, executes it on a real PostgreSQL database with 100,000+ rows, and automatically corrects errors in a retry loop if the query fails. Key features: 7-node LangGraph agent with conditional routing and self-correction pgvector semantic search for intelligent schema retrieval Full observability dashboard with real-time metrics and query history Trace replay — see every step the agent took to answer your question Deployed live: Next.js frontend on Vercel, FastAPI backend on Render Tech: LangGraph · Groq LLaMA 3.3 70B · PostgreSQL · pgvector · FastAPI · Next.js · Tailwind CSS Github: https://github.com/moiz-mansoori/QueryMind-NL2SQL-Agent-System

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Cover image for Built AIRO, a 6-agent ML

Built AIRO, a 6-agent ML automation system using LangGraph and Groq LLaMA 3.3. The system automates the entire ML pipeline from data ingestion to final report generation. Ran 18 experiments achieving RandomForest f1_macro of 0.7329. Features parallel training, MLflow tracking, SHAP explainability, and a dark Streamlit UI with live log streaming.

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Cover image for Built a deployed AI tool

Built a deployed AI tool that lets users upload research papers and query them using natural language. RAG pipeline with vector search retrieves precise context-aware answers. Features PDF parsing, AI summaries, topic modeling, and citation extraction. Live on Render.

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