You will get I will build AI apps using LLM, FastAPI, NLP and ML pipelines
Project details
I build production-ready AI applications using Python, FastAPI, Machine Learning, and LLMs.
With 8+ years of experience, I specialize in turning AI ideas into scalable backend systems — not just prototypes. I focus on clean architecture, reliable APIs, and optimized performance for real-world use.
You will get a well-structured AI solution tailored to your use case, whether it's an LLM-powered application, NLP pipeline, or ML-based backend service.
What sets me apart:
Production-grade systems (not experimental code)
Scalable API-first architecture
Focus on performance, reliability, and maintainability
If you're looking for a serious AI engineer who can deliver real, deployable systems — this project is for you.
With 8+ years of experience, I specialize in turning AI ideas into scalable backend systems — not just prototypes. I focus on clean architecture, reliable APIs, and optimized performance for real-world use.
You will get a well-structured AI solution tailored to your use case, whether it's an LLM-powered application, NLP pipeline, or ML-based backend service.
What sets me apart:
Production-grade systems (not experimental code)
Scalable API-first architecture
Focus on performance, reliability, and maintainability
If you're looking for a serious AI engineer who can deliver real, deployable systems — this project is for you.
AI Algorithms
AdaBoost, Autoencoder, Convolutional Neural Network, Feedforward Neural Network, Linear Discriminant Analysis, Long Short-Term Memory Network, Multimodal Large Language Model, Recurrent Neural Network, Regression Analysis, Transformer ModelAI Applications
AI Chatbot, AI Content Creation, AI-Enhanced Medical Imaging, Anomaly Detection, Conversational AI, Image Processing, Image Recognition, Machine Translation, Natural Language Generation, Natural Language Understanding, Time Series Analysis, Time Series ForecastingAI Development Language
PythonAI Tools
Azure OpenAI, GitHub Copilot, Hugging Face, Microsoft CNTK, NVIDIA AI Platform, PyTorch, Replit, Streamlit, TensorFlow, Word2vecAI Models
AlphaCode, BERT, BLOOM, ChatGPT, GPT-4, GPT-Neo, LaMDA, LLaMA, Midjourney AI, Naive Bayes Classifier, Stable Diffusion, WhisperWhat's included
| Service Tiers |
Starter
$120
|
Standard
$400
|
Advanced
$1,000
|
|---|---|---|---|
| Delivery Time | 4 days | 10 days | 21 days |
Number of Revisions | 1 | 2 | 3 |
AI Model Integration | |||
Batch Normalization | - | - | - |
Database Integration | - | ||
Detailed Code Comments | |||
Image Upscaling | - | - | - |
MLOps | - | - | |
Model Deployment | - | - | |
Model Documentation | - | - | - |
Model Monitoring | - | - | |
Model Testing & Optimization | - | - | |
Model Tuning | - | - | |
Natural Language Processing | - | ||
NLP Tokenization | - | - | - |
Pre-Training | - | - | - |
Prompt Engineering | - | ||
Setup File | |||
Source Code |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$75 - $250About Rahul
AI Engineer
Leeds, United Kingdom - 11:34 am local time
I help startups and businesses design, build, deploy, and improve AI-powered products using Python, FastAPI, LLMs, NLP, recommendation systems, forecasting models, and cloud-native infrastructure. My work goes beyond notebooks and prototypes — I build reliable systems that can run in production with proper APIs, monitoring, logging, deployment pipelines, and performance optimization.
My experience includes:
LLM and ML model integration into backend services
AI-powered APIs and production applications
NLP systems, text pipelines, and document processing
recommendation and ranking system architecture
forecasting and predictive modeling solutions
data pipelines, feature engineering, and model evaluation
Dockerized deployments, cloud infrastructure, and MLOps practices
observability, reliability improvements, and cost/latency optimization
I’ve led end-to-end development of AI products, from architecture and data models to production deployment and team mentoring. I’m comfortable working across the full lifecycle: problem framing, system design, implementation, testing, deployment, and iterative improvement in live environments.
Core stack: Python, FastAPI, SQL, LLMs, NLP, PyTorch, MLflow, Kafka, PySpark, Docker, AWS, Azure, MySQL, MongoDB, RDS, S3.
If you need someone who can turn AI ideas into production-ready systems — or improve an existing ML/LLM stack for reliability, scale, and performance — I’d be glad to help.
Steps for completing your project
After purchasing the project, send requirements so Rahul can start the project.
Delivery time starts when Rahul receives requirements from you.
Rahul works on your project following the steps below.
Revisions may occur after the delivery date.
Requirement Review
Understand your problem, goals, and technical requirements.
Solution Design
Define architecture, tools, and implementation approach.