You will get Multi Agent Real Estate AI With Full LLMOps
Project details
Most AI chatbots are simple API wrappers that hallucinate facts and fail at basic math. I build enterprise-grade, autonomous AI agents that solve complex business problems with zero hallucinations.
This project delivers a fully containerized, serverless AI Agent using LangGraph. What sets my work apart is the production-ready infrastructure: it includes a custom Retrieval-Augmented Generation (RAG) pipeline via Qdrant for accurate database searches, live web-scraping tools for real-time market data, and a sandboxed Python math evaluator to guarantee flawless financial calculations.
Deployed on Google Cloud Run for scale-to-zero cost efficiency, the system also features a complete LLMOps observability suite. You will receive a custom Grafana dashboard powered by BigQuery to track token costs, tool success rates, and P90 latency in real-time. You are not just buying a Python script; you are investing in a scalable, fully auditable AI microservice.
This project delivers a fully containerized, serverless AI Agent using LangGraph. What sets my work apart is the production-ready infrastructure: it includes a custom Retrieval-Augmented Generation (RAG) pipeline via Qdrant for accurate database searches, live web-scraping tools for real-time market data, and a sandboxed Python math evaluator to guarantee flawless financial calculations.
Deployed on Google Cloud Run for scale-to-zero cost efficiency, the system also features a complete LLMOps observability suite. You will receive a custom Grafana dashboard powered by BigQuery to track token costs, tool success rates, and P90 latency in real-time. You are not just buying a Python script; you are investing in a scalable, fully auditable AI microservice.
AI Algorithms
Large Language Model, Multimodal Large Language Model, Transformer ModelAI Applications
AI Chatbot, AI-Generated Code, AIOps, Conversational AI, Natural Language Generation, Natural Language Understanding, Synthetic Data Generation, Text RecognitionAI Development Language
PythonAI Tools
GitHub Copilot, Hugging Face, StreamlitAI Models
BERT, LLaMAWhat's included $50
These options are included with the project scope.
$50
- Delivery Time 2 days
- Number of Revisions 1
- AI Model Integration
- Database Integration
- Detailed Code Comments
- MLOps
- Model Deployment
- Model Documentation
- Model Monitoring
- Model Testing & Optimization
- Natural Language Processing
- NLP Tokenization
- Prompt Engineering
- Setup File
- Source Code
Optional add-ons
You can add these on the next page.
Fast 1 Day Delivery
+$10
Additional Revision
+$25Frequently asked questions
About Jaynil
Experienced ML & SWE | MSCS UCSD | Ex-Oracle | Cloud, MLOps, LLMs
Raleigh, United States - 2:31 am local time
What I can bring to your Upwork projects:
1) Full-Stack ML Solutions & MLOps:
- Develop interactive AI agents leveraging Retrieval Augmented Generation (RAG) pipelines with modern LLMs (e.g., quantized Zephyr-7B-beta via CTransformers), semantic search, and vector databases like ChromaDB.
- Architect, implement, and deploy scalable MLOps infrastructure on cloud platforms like AWS, utilizing Kubernetes for orchestrating model training workloads and reliable application deployment.
- Led the design and development of a public-facing web application for an AutoML platform (Node.js backend, React.js frontend), engineering solutions for horizontal scaling and high availability.
2) Software & Cloud Engineering:
- Design and implement critical cloud networking features, such as CIDR-based route filtering for Oracle's Dynamic Routing Gateway (DRG), involving API layer modifications, Swagger spec updates, SDK generation (Terraform, Go, Python, CLI), and robust testing.
- Successfully deployed code changes globally across 18+ OCI regions using internal DevOps tooling with Docker and Terraform scripts.
- Built impactful backend tools, for instance, a Go-based "Fleet Visualizer and Restarter" for real-time monitoring and selective restarts of DRG routers, which reduced incident response time and improved fleet health visibility.
- Engineered high-throughput ETL pipelines capable of handling up to 1 million updates/sec for collecting and storing router metrics into Redis and Aerospike.
3) Deep Learning & Advanced Research:
- Developed a deep learning pipeline to emulate complex physics-based simulators (e.g., QUIC-Fire for fire spread modeling), using a 234GB dataset and achieving a 66.67% reduction in MSE with a Physics-Guided loss in TensorFlow. This included proposing a novel RoS calculation strategy and implementing custom dataloaders for ConvLSTM training.
- Contributed to published research (ACPR 2019) on developing ensemble evaluation frameworks using Conditional GANs to automatically score speech quality in TTS systems.
Core Technical Strengths:
Languages: Python, Golang, Java, C++, TypeScript, Bash
Big Data & ML: TensorFlow, PyTorch, OpenCV, LangChain, MLFlow, PySpark, Kafka, Apache Airflow, DaskML, CTransformers, spaCy
Databases: MongoDB, Postgres, Snowflake, Redis, ChromaDB, AeroSpike
Cloud & DevOps: Docker, Terraform, Kubernetes, Grafana, ELK, AWS, Oracle Cloud Infrastructure (OCI)
I am actively seeking remote, part-time contract opportunities where I can apply my skills in machine learning, MLOps, cloud engineering, and backend development to help you achieve your project goals. Let's connect to discuss how I can contribute to your success!
Steps for completing your project
After purchasing the project, send requirements so Jaynil can start the project.
Delivery time starts when Jaynil receives requirements from you.
Jaynil works on your project following the steps below.
Revisions may occur after the delivery date.
Architecture Review & Data Audit
We review your specific business requirements, identify the data sources you want the agent to search (CSV, PDFs, databases), and define the exact custom tools the agent will need.
RAG Pipeline & Vector DB Setup
I provision a Qdrant Cloud vector database, generate semantic embeddings using HuggingFace models, and ingest your business data so the agent can accurately retrieve it.