Ishipprivacy-firstAIsystemsandthefull-stackproductsaroundthem.

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• Who is M. Danish Bashir?

  M. Danish Bashir is a senior AI engineer and full-stack developer based in Lahore, Pakistan, working remotely. He has over 4 years in AI and machine learning and over 8 years in software engineering, and has shipped 20+ production systems spanning agentic AI platforms, RAG pipelines, computer vision, and voice.

• What does privacy-first AI mean?

  Privacy-first AI means running inference on-device or self-hosted rather than sending user data to a third-party model API. In practice that means quantized local models, offline text-to-speech, and self-hosted vector search — so sensitive data never leaves the environment that owns it.

• What AI and machine learning services do you offer?

  RAG pipeline design and implementation, LLM agent systems built with LangChain, LangGraph, CrewAI and MCP, computer vision including face recognition and anti-spoofing, emotion-aware text-to-speech, and the MLOps and cloud infrastructure to run all of it in production.

• What is your technical stack?

  Python and PyTorch with Hugging Face for models, LangChain and LangGraph for agent orchestration, FastAPI and Django on the backend, Next.js and React on the frontend, Postgres with Pinecone or ChromaDB for vector search, and Docker, AWS and Kubernetes for deployment.

• Are you available for freelance or contract work?

  Yes. I take on select engagements — contract AI engineering, architecture and code review, and full production builds. I also co-found a small AI software studio of two senior ML engineers and one senior web developer for larger end-to-end projects.

• Do you work with remote and international teams?

  Yes. I work fully remotely from Lahore, Pakistan, and have delivered production systems for distributed teams across cybersecurity, supply chain, and HR technology.

• What results have your systems delivered?

  On the FourKites agentic workforce, the systems cut exception handling by roughly 85% and reduced client IT costs by up to 75%. Across engagements I have shipped more than 20 production systems and mentored two AI and ML cohorts.

• How does an engagement usually work?

  Five stages: discovery and scoping to agree on goals and success metrics, architecture and a prototype to de-risk the hardest part, build and integration across models, pipelines, backend and UI, deploy and harden with monitoring in place, then ongoing support and iteration against real production feedback.