AI Developer
LLM integrations, custom NLP, and predictive analytics. Stop playing with chat interfaces and build real AI tools.
Why hire Nimesh as your AI Developer?
I bridge the gap between academic machine learning and product engineering. I build AI features that actually ship to production and solve business problems.
✓RAG Implementations: Connect GPT-4 or Claude to your proprietary database to stop hallucinations.
✓Custom ML Models: Training scikit-learn or TensorFlow models for specific classification tasks.
✓API Deployment: Wrapping AI models in FastAPI and deploying them securely via Docker.
✓Data Extraction: Turning unstructured PDFs and web pages into clean, structured JSON.
AI Developer Skills
How to interview a AI Developer
Client Success Stories
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Nimesh developed our GymTaar mobile application with exceptional professionalism and technical expertise. He understood our business requirements quickly, implemented every feature efficiently, and delivered a smooth, user-friendly experience for both trainers and members. His communication, problem-solving ability, and commitment to quality made the entire development process seamless.
Founder, GymTaar
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We partnered with Nimesh to build the BabalCloud website, and the results exceeded our expectations. He created a modern, responsive, and high-performing platform that accurately represents our brand. His attention to detail, design sense, and technical knowledge helped us launch a professional online presence that our customers love.
Founder, BabalCloud
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Nimesh successfully designed and developed our Insuretech Nepal website with a strong focus on performance, usability, and scalability. He transformed our vision into a professional digital platform while maintaining excellent communication throughout the project. We highly recommend him to any organization seeking a reliable and skilled software developer.
CEO, Insuretech Nepal
Companies I've Worked With
AI Developer FAQs
Usually, training from scratch isn't necessary. We use RAG (Retrieval-Augmented Generation) to give a pre-trained model access to your documents, which is faster, cheaper, and more accurate.
Basic LLM integrations (like document summarization) start around $1,500. Custom ML pipelines or complex RAG systems start around $4,000.
Yes. Given the sensitive nature of company data used in AI, strict NDAs are mandatory.
Python is mandatory for AI. I use FastAPI for the backend endpoints, LangChain for LLM orchestration, and whatever frontend technology fits your product.
No. When using enterprise APIs (like OpenAI's paid API), your data is explicitly NOT used to train their global models. It is strictly confidential.
Ready to turn your ideas into reality? Fill out the form below with your project details, and I will get back to you within 24 hours with a free consultation and quote.