You will get an LLMOps pipeline with monitoring, tracing, and evaluation setup
Project details
You will get a complete LLMOps pipeline designed to monitor, trace, evaluate, and optimize your AI applications in production. I build end-to-end observability systems for LLM applications so you can track performance, reduce hallucinations, monitor costs, and improve reliability.
This includes tracing, prompt versioning, output evaluation, latency monitoring, drift detection, failure analysis, and automated alerts. Whether you’re running AI chatbots, RAG systems, multi-agent workflows, or enterprise AI apps, I can build the infrastructure to manage them efficiently.
With expertise in LangSmith, Arize Phoenix, MLflow, Weights & Biases, and enterprise AI monitoring tools, I help businesses improve model quality, compliance, and production stability while scaling confidently.
This includes tracing, prompt versioning, output evaluation, latency monitoring, drift detection, failure analysis, and automated alerts. Whether you’re running AI chatbots, RAG systems, multi-agent workflows, or enterprise AI apps, I can build the infrastructure to manage them efficiently.
With expertise in LangSmith, Arize Phoenix, MLflow, Weights & Biases, and enterprise AI monitoring tools, I help businesses improve model quality, compliance, and production stability while scaling confidently.
AI Algorithms
AdaBoost, AlexNet, Convolutional Neural Network, CycleGAN, Feedforward Neural Network, Large Language Model, Multimodal Large Language Model, Recurrent Neural Network, Regression Analysis, YOLOAI Applications
AI Chatbot, AI Mobile App Development, AIOps, Anomaly Detection, Automatic Speech Recognition, Facial Recognition, Machine Translation, Natural Language Generation, Natural Language Understanding, Neural Machine Translation, Neural Style Transfer, Sentiment AnalysisAI Development Language
PythonAI Tools
Adobe Firefly, Azure OpenAI, Bing AI, Copy.ai, GitHub Copilot, Hugging Face, Microsoft CNTK, NVIDIA AI Platform, PyTorch, TensorFlowAI Models
AlphaCode, BERT, ChatGPT, DALL-E, GPT-3, GPT-4, GPT-J, GPT-Neo, LaMDA, LLaMA, Midjourney AI, OpenAI CodexWhat's included
| Service Tiers |
Starter
$1,200
|
Standard
$2,000
|
Advanced
$4,000
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 10 days |
Number of Revisions | 2 | 3 | 5 |
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 | - | - | - |
About Bhupinder Singh
Agentic AI Architect | Multi-Agent Systems | RAG | LLMOps
Johnston, United States - 10:34 pm local time
With 5+ years of AI/ML experience and 30+ successful AI deployments, I specialize in building intelligent systems using LangGraph, CrewAI, AutoGen, AWS Bedrock, Azure OpenAI, and Vertex AI.
𝗠𝘆 𝗲𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲 𝗰𝗼𝘃𝗲𝗿𝘀 𝘁𝗵𝗲 𝗰𝗼𝗺𝗽𝗹𝗲𝘁𝗲 𝗔𝗜 𝗹𝗶𝗳𝗲𝗰𝘆𝗰𝗹𝗲:
✔ Multi-Agent System Design
✔ RAG (Retrieval-Augmented Generation) Pipelines
✔ MCP Server Development & Tool Integration
✔ AI Automation Workflows
✔ LLMOps, Monitoring & Evaluation
✔ AI Governance, Guardrails & Compliance
✔ Enterprise AI Infrastructure on AWS, GCP & Azure
I build AI systems that do more than just chat.
𝗥𝗲𝗰𝗲𝗻𝘁 𝘄𝗼𝗿𝗸 𝗶𝗻𝗰𝗹𝘂𝗱𝗲𝘀:
• Autonomous Loan Underwriting Agent (78% faster decisions)
• Clinical Knowledge Assistant for Hospital Chains (40K+ documents indexed)
• AI Customer Support Automation Platform ($1.2M annual savings)
• Contract Intelligence Platform (90% review time saved)
• Agentic Market Research Systems (85% analyst time saved)
𝗠𝘆 𝘁𝗲𝗰𝗵 𝘀𝘁𝗮𝗰𝗸 𝗶𝗻𝗰𝗹𝘂𝗱𝗲𝘀:
→ LangGraph, CrewAI, AutoGen
→ AWS Bedrock, SageMaker
→ Azure OpenAI
→ Vertex AI
→ Claude, GPT-4o, Gemini, Llama
→ Pinecone, Weaviate, pgvector
→ LangSmith, Arize Phoenix
→ Terraform, CI/CD, MLflow
If you're looking to build autonomous AI agents, enterprise RAG systems, or AI workflow automation, I can help you architect, build, and deploy scalable solutions end-to-end.
Steps for completing your project
After purchasing the project, send requirements so Bhupinder Singh can start the project.
Delivery time starts when Bhupinder Singh receives requirements from you.
Bhupinder Singh works on your project following the steps below.
Revisions may occur after the delivery date.
Step 1: Workflow Analysis
Review your LLM application, prompts, and production setup
Step 2: Observability Architecture
Design monitoring, tracing, and evaluation framework