You will get Agentic AI Evaluator for Human-AI Answer Quality (Multi-Agent RAG Scoring)
Project details
You’ll get a cutting-edge Agentic AI system that transforms how your business operates using autonomous LLM-driven agents. With 5+ years of hands-on experience in deploying production-grade AI pipelines, I specialize in building smart, explainable, and fully orchestrated AI workflows—combining MLOps, Bedrock Agents, SageMaker, MCP, and RAG strategies.
Whether it’s automating document intelligence, evaluating RAG outputs, or building a “Model to API in 7 Days” MVP, I deliver solutions that scale, self-optimize, and support enterprise reliability. Everything I build is modular, API-ready, and equipped with monitoring and retraining hooks—ready for real-world impact.
Whether it’s automating document intelligence, evaluating RAG outputs, or building a “Model to API in 7 Days” MVP, I deliver solutions that scale, self-optimize, and support enterprise reliability. Everything I build is modular, API-ready, and equipped with monitoring and retraining hooks—ready for real-world impact.
AI Algorithms
Large Language Model, Multimodal Large Language Model, Transformer ModelAI Applications
AI Content Creation, AI Text-to-Image, AI Text-to-Speech, AI-Enhanced Medical Imaging, Anomaly Detection, Automatic Speech Recognition, Machine Translation, Natural Language Generation, Natural Language Understanding, Neural Machine Translation, Sentiment Analysis, Speech SynthesisAI Development Language
PythonAI Tools
GitHub Copilot, Gradio, Hugging Face, NVIDIA AI Platform, PyTorch, Streamlit, TensorFlowAI Models
ChatGPT, GPT-3, GPT-4, LLaMA, OpenAI CodexWhat's included
| Service Tiers |
Starter
$200
|
Standard
$350
|
Advanced
$999
|
|---|---|---|---|
| Delivery Time | 5 days | 12 days | 25 days |
Number of Revisions | 1 | 4 | 8 |
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.
Test Harness / Evaluation Agent
+$100Frequently asked questions
About Rehan Fazal
8+ Years Senior Agentic AI Architect | Multi-Agent Systems | MCP A2A|
New Delhi, India - 10:11 am local time
Agentic AI Platforms | Multi-Agent Collaboration | Agent Swarms | Planning & Reasoning | Tool Calling | Memory & Context Management | Long-Running Sessions | Observability & Guardrails
AWS Bedrock AgentCore | Azure AI Foundry Agent Service | LangChain Agents | AutoGen | CrewAI | AgentScope | LangGraph | Camel | Swarm | MCP | Vector Databases | API Integrations
Python | MLOps | MLflow | SageMaker | Kubernetes (EKS) | Docker | Terraform | Event-Driven Architectures | Cloud-Native Microservices | Enterprise LLM Deployments | Governance & Monitoring
8+ years of experience in Agentic AI Infrastructure & Scale — I specialize in leveraging enterprise-grade agent platforms such as AWS Bedrock AgentCore and Microsoft Azure AI Foundry Agent Service to deploy production-ready multi-agent systems with full lifecycle support (memory, tool integration, identity, observability, long-running sessions, and scaling). By offloading infrastructure and orchestration overhead to these platforms, I focus on high-impact areas like agent design, reasoning, workflow logic, and tool integrations — delivering robust, scalable, and secure autonomous AI systems.
Agentic AI | Multi-Agent Collaboration at Scale
My core area of expertise is in building Agentic AI frameworks where autonomous agents—powered by LLMs—collaborate, plan, reason, and take actions based on tool integrations, memory, and contextual goals.
I’ve deployed real-world systems using leading frameworks like:
AWS Agentcore
Azure AI Foundry
LangChain – for modular agent planning, memory, tools
AutoGen (Microsoft) – for multi-agent dialogue orchestration
CrewAI – for role-based agent workflows
AgentScope – for secure, asynchronous agent execution with traceability
Model Context Protocol (MCP) – for managing agent state and orchestration in production
Camel, Swarm, LangGraph – for various use cases like teamwork, reactivity, and stateful logic
These frameworks let me engineer intelligent workflows where agents automate tasks, analyze data, chat with users, trigger APIs, and collaborate across enterprise environments like AWS Bedrock, Dataiku, and Kubernetes.
🛠️ Core Technologies & Frameworks
I bring full-stack AI engineering skills spanning:
Agentic AI: MCP, LangChain, AutoGen, CrewAI, AgentScope, LangGraph, Camel, Swarm
LLMs & Optimization: GPT-4, Claude 3, Titan, LLaMA 2, LoRA, PEFT, Quantization, Distillation
Multimodal AI: Vision Transformers, CLIP, YOLOv8, GANs, AWS Rekognition, OCR
MLOps Tools: MLflow, SageMaker, Kubeflow, Dataiku, GitHub Actions, Terraform, Docker, Kubernetes (EKS), LakeFS
Cloud Platforms: AWS (Bedrock, Lambda, S3, CloudWatch, EventBridge), Azure, GCP
CI/CD & Automation: Airflow, CodePipeline, GitOps, Monitoring & Drift Detection
🏗️ Systems I've Built (Real-World Impact)
🔹 SAGEAI (Penske Transport | Genpact)
Built a fully autonomous Agentic AI platform using AWS Bedrock Agents, Claude + Titan models, and multi-agent orchestration. It interprets diagnostic codes, plans actions, calls APIs, and triggers real-time vehicle maintenance workflows. Integrated Bedrock Knowledge Bases for real-time search and retrieval with dynamic agent chains.
🔹 Vehicle Defect Detection System (Penske | Genpact)
Developed an event-driven defect detection pipeline for vehicle engines. Used AWS Lambda, EKS, XGBoost/TextCNN hybrid models, and Dataiku pipelines to reduce fleet downtime with 24/7 monitoring—fully integrated with ticketing systems and auto-triggered repair actions.
🔹 Sony India – Edge Vision + LLM Integration
Optimized LLM and GAN pipelines for Bravia and Alpha series. Enhanced smart TV contextual scene understanding and NLP-driven content recommendations, with model distillation and inference optimizations for edge deployment.
🎯 What Makes Me Different
I don’t just run models—I build agents that reason, learn, and act autonomously.
I deeply understand Agentic AI architecture, not just prompting or LLM fine-tuning.
I create resilient, reusable pipelines using modular agents, MCP orchestration, and bedrock workflows.
I write production-grade infra code, CI/CD automation, model evaluation logic, and dashboards—end-to-end ownership from prototype to deployment.
I’ve worked with multi-agent chains, graph-based plans, and asynchronous agents in both cloud-native and hybrid setups.
💡 Let’s Build the Future of Autonomous Systems
If your project needs:
Autonomous AI agents that do more than just chat
Multi-agent collaboration and tool chaining
Industrial AI (Transport, IoT, Vision, Text, Speech)
Complex MLOps automation pipelines
Enterprise LLM deployments with governance, retraining, and monitoring
Then I’d love to partner with you.
📩 Let’s connect and build intelligent systems that think, act, and evolve.
Steps for completing your project
After purchasing the project, send requirements so Rehan Fazal can start the project.
Delivery time starts when Rehan Fazal receives requirements from you.
Rehan Fazal works on your project following the steps below.
Revisions may occur after the delivery date.
Discovery & Input Review
Review your use case, input data (PDFs, images, docs, etc.), and target outcomes.
Agent Design & Architecture
Define autonomous agent roles, memory strategy, flow logic, and model integration (Claude, GPT-4, Titan, etc.).