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Danish A. - Django Developer, React Native, nextjs

My name is Danish A. and I have over 6 years years of experience in the tech industry. I specialize in the following technologies: Full-Stack Development, Back-End Development, Python, FastAPI, Django, etc.. I hold a degree in Bachelor's degree. Some of the notable projects I’ve worked on include: AI Agent - Skip, Dev Lead and Support Specialist - VistaBee, Agent Forge SaaS Platform, Call Scheduling Receptionist (Vapi, n8n, Airtable), Advanced RAG Pipeline, etc.. I am based in Islamabad, Pakistan. I've successfully completed 17 projects while developing at Softaims.

I approach every technical challenge with a mindset geared toward engineering excellence and robust solution architecture. I thrive on translating complex business requirements into elegant, efficient, and maintainable outputs. My expertise lies in diagnosing and optimizing system performance, ensuring that the deliverables are fast, reliable, and future-proof.

The core of my work involves adopting best practices and a disciplined methodology, focusing on meticulous planning and thorough verification. I believe that sustainable solution development requires discipline and a deep commitment to quality from inception to deployment. At Softaims, I leverage these skills daily to build resilient systems that stand the test of time.

I am dedicated to making a tangible difference in client success. I prioritize clear communication and transparency throughout the development lifecycle to ensure every deliverable exceeds expectations.

Main technologies

  • Django Developer

    6 years

  • Full-Stack Development

    1 Year

  • Back-End Development

    3 Years

  • Python

    2 Years

Additional skills

Direct hire

Potentially possible

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Experience Highlights

AI Agent - Skip

This AI-powered agent is designed to support TrainingPeaks (TP) athletes and coaches. It provides a chat-like interface that allows athletes to stay updated on their workout plans and receive personalized recommendations to enhance performance based on their TP data. For coaches, the platform includes a dedicated dashboard where they can manage athlete training plans, customize communication settings for each athlete, and create reusable message templates. The core objective is to streamline communication and planning for both athletes and coaches through intelligent automation.

Dev Lead and Support Specialist - VistaBee

I provided technical support to vistaBee to ensure operational continuity of their platform. I also led their development team to add new features, resolve critical bugs, improve security, optimize cloud cost, and ensure consistent DevOps practices. My key contributions included: 1. Provide daily technical support, add new features, resolve bugs 2. Document business processes 4. Enhance platform security 5. Optimize cloud infrastructure to reduce costs (helped reduce monthly cost from $9000 to $2500) 6. Improve DevOps workflows and code quality 7. Migrate existing data to a new platform

Agent Forge SaaS Platform

Agent Forge is a SaaS platform that lets you create, customize, and deploy AI-powered agents—chatbots, inbound call assistants, and website voice agents—without worrying about hosting, servers, or infrastructure. You bring your own services like Twilio, Deepgram, OpenAI, or other LLM APIs, and we take care of the rest. From hosting and storage to integrations and scaling, Agent Forge provides everything under one roof so you can focus on growth, not setup.

Call Scheduling Receptionist (Vapi, n8n, Airtable)

Manual scheduling is time-consuming, error-prone, and limited to business hours. This solution enables a 24/7 voice-based scheduling system that reduces workload for staff, minimizes booking errors, and improves customer experience by providing instant, voice-driven scheduling over the phone. It also captures valuable insights such as call summaries, recordings, and costs for analysis and logs these entries into Airtable Base.

Advanced RAG Pipeline

Built a context-aware chatbot using a RAG Pipeline. The system processes a pdf documents, extracts text data, and generates semantic embeddings using a BERT-based model. These embeddings are stored in a FAISS vector store for efficient similarity-based retrieval. When a user submits a query, the chatbot retrieves semantically relevant passages from the vector store. It verifies context relevance, rewrites the query if needed, and passes the context to an LLM and uses chain-of-reasoning to generate a response. A final RAGASS check ensures the answer is grounded and not hallucinated.

Education

  • Lahore University of Management Sciences (LUMS)

    Bachelor's degree in Computer science

    2014-01-01-2018-01-01

Languages

  • English

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