You will get LLM Integration for Your App: OpenAI, Claude, or Gemini API
Project details
You already have a product I add the AI layer that makes it smarter. Summarization, classification, semantic search, smart replies, content generation: whatever fits your use case, integrated cleanly into your existing backend.
Adding AI to a live product is mostly engineering, not prompting. The real work is in the integration: rate limiting, retries, token-cost control, streaming, caching, and graceful fallback when the model fails. I treat the LLM as one unreliable dependency among many and build around it accordingly, so the feature is fast and doesn't surprise you with the bill.
You'll get the feature integrated into your backend, prompts hardened against edge cases, cost monitoring, and streaming where it improves the experience. Works with your existing stack.
7+ years building Python and AI systems. Message me with the feature you have in mind.
Adding AI to a live product is mostly engineering, not prompting. The real work is in the integration: rate limiting, retries, token-cost control, streaming, caching, and graceful fallback when the model fails. I treat the LLM as one unreliable dependency among many and build around it accordingly, so the feature is fast and doesn't surprise you with the bill.
You'll get the feature integrated into your backend, prompts hardened against edge cases, cost monitoring, and streaming where it improves the experience. Works with your existing stack.
7+ years building Python and AI systems. Message me with the feature you have in mind.
AI Development Type
Deep Learning, Knowledge Representation, Recommendation System, Software MaintenanceAI Tools
Azure Machine Learning, deeplearn.js, Deeplearning4j, Google AutoML, MLflow, OpenCV, PyBrain, PyTorch, Sonnet, TensorFlowAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$350
|
Standard
$850
|
Advanced
$1,600
|
|---|---|---|---|
| Delivery Time | 4 days | 10 days | 21 days |
Number of Revisions | 1 | 2 | 3 |
AI Model Integration | |||
Detailed Code Comments | - | ||
Knowledge Graph | - | - | - |
Model Documentation | |||
Ontology | - | - | - |
Source Code | - | ||
Taxonomy | - | - | - |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$500 - $1,500
Additional Revision
+$150Frequently asked questions
3 reviews
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SE
Sean E.
Jun 7, 2026
Looking for Senior AI Backend Engineer – Conversational AI Platform
Jazay was great to work with again. He brings a strong mix of technical expertise, adaptability and clear communication, and was a trusted partner throughout the project. He stayed solution oriented from scoping through delivery, took ownership of outcomes, and kept things moving without overcomplicating the process. Would highly recommend reaching out and leaning on him for your project.
SE
Sean E.
Sep 13, 2024
Chatbot Pt 3
Jazay provides a rare combination of expertise, adaptability and quality of work. We built a dynamic LLM chatbot with custom logs, admin panels, prompt inputs - and Jazay was a trusted partner from scoping to go-live. Would highly recommend reaching out and leaning on him for your project!
SE
Sean E.
Jun 15, 2024
Chatbot Pt 2
Jazay provided a rare combination of expertise and high quality execution. He provided detailed, realistic and reasonable scopes of work - and helped build an LLM architecture with multiple complex requirements. Highly recommend choosing Jazay if you’re looking for a trustworthy development partner.
About Jazay
Generative AI Developer | LLM & RAG Systems | Python Backend
100%
Job Success
Peshawar, Pakistan - 7:36 am local time
CORE STRENGTHS
LLM Integration: OpenAI, Claude, Gemini, and open-source models wired into real products, not demos
RAG Systems: retrieval pipelines with vector databases (Qdrant, Pinecone, Weaviate) for accurate document Q&A and semantic search
AI Agents: autonomous systems with multi-step reasoning, tool calling, and API execution
Production Deployment: AWS, Docker, and CI/CD for reliable, maintainable systems
Business focus: every build ties to a measurable outcome — lower response times, reduced manual work, faster retrieval
ABOUT ME
I'm a backend and AI engineer with 7+ years of professional experience building systems that solve real business problems. My work spans large language models, RAG, and full-stack Python development. At TalentPop I led AI initiatives across property tech and customer-service automation, shipping production systems used daily by real teams. My approach pairs solid engineering with business sense I want to understand the outcome you're after before I write a line of code.
SERVICES I DELIVER
LLM Integration & RAG Systems: Retrieval-Augmented Generation pipelines combining vector databases with language models for accurate document Q&A, knowledge retrieval, and context-aware responses.
Custom AI Agent Development: Autonomous systems capable of multi-step reasoning, API integration, and real-time decision execution. Ideal for lead qualification, support automation, and workflow orchestration.
Production Chatbot Development: Multi-channel conversational AI (web, WhatsApp, Telegram) with NLP understanding, sentiment analysis, and intelligent responses for customer engagement.
End-to-End ML Solutions: Predictive models and computer vision systems deployed with monitoring and retraining pipelines for continuous improvement.
PROBLEMS I SOLVE
Manual, repetitive support work → AI systems that handle routine conversations automatically and free up your team
Unstructured data nobody can search → RAG systems enabling fast semantic search across large document sets
Workflows that need constant manual oversight → AI agents that execute multi-step processes reliably
Raw data sitting unused → ML models that turn it into usable predictions and insight
TECH STACK
Languages: Python, TypeScript, JavaScript, Node.js
Web: FastAPI, Flask, Django, Nest.js
AI/ML: LangChain, Hugging Face, TensorFlow, PyTorch, Scikit-Learn
LLMs: OpenAI, Anthropic Claude, Google Gemini, RASA
Vector DBs: Qdrant, Pinecone, Weaviate
Data: PostgreSQL, MongoDB, Redis, Apache Kafka
Cloud/DevOps: AWS, GCP, Docker, Kubernetes, Terraform, GitHub Actions
Let's talk about what you're trying to build.
Steps for completing your project
After purchasing the project, send requirements so Jazay can start the project.
Delivery time starts when Jazay receives requirements from you.
Jazay works on your project following the steps below.
Revisions may occur after the delivery date.
Review your app & the feature
You and me confirm the feature, the expected input/output, and I review your stack to plan a clean integration.
Build & harden the integration
I wire the AI feature into your backend with retries, rate limiting, and prompts hardened against edge cases