You will get Backend & AI-Integrated Web Applications
Project details
You will get a production-ready backend system tailored to your business needs. I specialize in Python/FastAPI, Node.js, Laravel, and AI-integrated applications to deliver high-performance APIs, automated workflows, and intelligent systems. With extensive experience in SaaS platforms, dashboards, booking systems, and AI automation, I ensure your project is secure, scalable, and user-friendly. All solutions are clean, maintainable, and optimized for business impact, not just code.
Programming Languages
HTML & CSS, JavaScript, PythonCoding Expertise
Localization, Performance Optimization, DesignWhat's included $300
These options are included with the project scope.
$300
- Delivery Time 10 days
- Number of Revisions 2
- Design Customization
- Content Upload
- Responsive Design
- Source Code
About Rabia
AI & Machine Learning Engineer | GenAI, LLMs, AI Agents, Python
Islamabad, Pakistan - 6:26 am local time
I specialize in taking ML systems from concept → training → optimization → deployment → monitoring, with a strong focus on 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝗱𝗮𝘁𝗮, 𝗹𝗼𝘄-𝗹𝗮𝘁𝗲𝗻𝗰𝘆 𝗶𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲, and 𝘀𝗰𝗮𝗹𝗮𝗯𝗹𝗲 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁.
Whether you need a 𝗰𝘂𝘀𝘁𝗼𝗺 𝗠𝗟 𝗺𝗼𝗱𝗲𝗹, 𝗳𝗶𝗻𝗲-𝘁𝘂𝗻𝗲𝗱 𝗟𝗟𝗠, 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗩𝗶𝘀𝗶𝗼𝗻 𝘀𝘆𝘀𝘁𝗲𝗺, 𝗥𝗔𝗚-𝗯𝗮𝘀𝗲𝗱 𝗔𝗜 𝘀𝗲𝗿𝘃𝗶𝗰𝗲, or 𝗔𝗣𝗜-𝗱𝗿𝗶𝘃𝗲𝗻 𝗠𝗟 𝗶𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲, I deliver 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻-𝗴𝗿𝗮𝗱𝗲 solutions not research demos.
𝗪𝗵𝗮𝘁 𝗜 𝗗𝗼 𝗕𝗲𝘀𝘁
𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 & 𝗔𝗜 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴
End-to-end Machine Learning system development
AI Engineer / ML Engineer for production systems
Model training, fine-tuning & optimization
Feature extraction, engineering & selection
Hyperparameter tuning & evaluation
Transfer learning & parameter-efficient fine-tuning
Production model validation & performance testing
𝗟𝗟𝗠𝘀, 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 & 𝗥𝗔𝗚 𝗦𝘆𝘀𝘁𝗲𝗺𝘀
Fine-tuning open-source & commercial LLMs
LLM-based reasoning & deterministic outputs
RAG systems (embeddings, vector databases, semantic retrieval)
AI agents & autonomous task execution
Multi-agent systems & agent orchestration
Prompt engineering, evaluation & guardrails
Context-aware chatbots & copilots
Zero-shot & few-shot inference strategies
𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗩𝗶𝘀𝗶𝗼𝗻, 𝗢𝗖𝗥 & 𝗠𝘂𝗹𝘁𝗶𝗺𝗼𝗱𝗮𝗹 𝗔𝗜
Object detection & image analysis (YOLO, CNNs)
Image classification & anomaly detection
OCR & document understanding pipelines
Real-world dataset handling (image, audio, text)
Dataset taxonomy, labeling & annotation workflows
Precision / Recall / F1 optimization for safety-critical systems
Model robustness under real-world variability
𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 & 𝗠𝗟 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀
Data ingestion, cleaning & validation
Structured & unstructured data processing
ML training pipelines (batch & real-time)
Feature stores & dataset versioning
Feedback loops for continuous improvement
Statistical analysis & dataset quality audits
𝗔𝗣𝗜𝘀, 𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 & 𝗕𝗮𝗰𝗸𝗲𝗻𝗱 𝗔𝗜 𝗦𝗲𝗿𝘃𝗶𝗰𝗲𝘀
Python-based backend AI services
FastAPI inference APIs
Deterministic JSON outputs & confidence scoring
Latency optimization & throughput tuning
CRM, ERP & third-party system integrations
Scalable, stateless ML microservices
𝗖𝗹𝗼𝘂𝗱, 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 & 𝗠𝗟𝗢𝗽𝘀
AWS, Google Cloud (GCP) & Azure deployments
GPU-enabled & cloud-native ML services
Dockerized ML workloads
CI/CD for ML & model versioning
Monitoring, logging & drift detection
Cost-optimized inference & scaling strategies
Production reliability & observability
𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 & 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗥𝗲𝗮𝗱𝗶𝗻𝗲𝘀𝘀
Low-latency inference optimization
Scalable production architectures
Model performance benchmarking
Monitoring ML systems in production
Secure, stable & maintainable deployments
𝗧𝗲𝗰𝗵 𝗦𝘁𝗮𝗰𝗸
𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲𝘀: Python
𝗠𝗟 & 𝗔𝗜: Machine Learning, Deep Learning, LLMs, RAG, AI Agents, NLP, Computer Vision
𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀: PyTorch, TensorFlow, Hugging Face
𝗕𝗮𝗰𝗸𝗲𝗻𝗱: FastAPI, REST APIs
𝗩𝗲𝗰𝘁𝗼𝗿 𝗗𝗕𝘀: Pinecone, Weaviate, Supabase
𝗖𝗹𝗼𝘂𝗱 & 𝗠𝗟𝗢𝗽𝘀: AWS, GCP, Azure, Docker, CI/CD
𝗪𝗵𝘆 𝗖𝗹𝗶𝗲𝗻𝘁𝘀 𝗖𝗵𝗼𝗼𝘀𝗲 𝗠𝗲
Production-first Machine Learning Engineer
End-to-end ownership (data → model → deployment)
Strong systems & performance thinking
Practical problem-solving over theory
Clear communication & async-friendly delivery
ML solutions designed for real business impact
𝗛𝗼𝘄 𝗜 𝗪𝗼𝗿𝗸
Deep requirement analysis & ML feasibility checks
Clear milestones & evaluation metrics
Async collaboration (Slack, Notion, Loom)
Clean documentation & handoff
Long-term optimization & support
𝗞𝗲𝘆𝘄𝗼𝗿𝗱𝘀
Machine Learning Engineer, AI Engineer, LLM Engineer, Python ML Developer, Model Training, Fine-Tuning, RAG Systems, AI Agents, Computer Vision, OCR, NLP, FastAPI, ML Pipelines, Cloud ML Deployment, Production AI, Scalable Inference, MLOps
𝗛𝗮𝘀𝗵𝘁𝗮𝗴𝘀
#MachineLearningEngineer #AIEngineer #LLM #RAG #AIAgents #Python
#ProductionAI #MLOps #CloudML #ComputerVision #FastAPI
#ScalableAI #MLPipelines #AIForBusiness
If you’re looking for a 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻-𝗴𝗿𝗮𝗱𝗲 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 who can design, fine-tune, deploy, and scale real AI systems let’s talk.
Steps for completing your project
After purchasing the project, send requirements so Rabia can start the project.
Delivery time starts when Rabia receives requirements from you.
Rabia works on your project following the steps below.
Revisions may occur after the delivery date.
Project Analysis & Planning
Review the client’s specifications, identify key backend requirements, define database structure, and plan API endpoints and AI integration workflows.