You will get a production-ready AI agent that automates your workflows end-to-end
Project details
You'll get an AI agent that actually holds up in production, not a clever demo that breaks on the second edge case.
I'm a senior AI architect specializing in agentic systems, LLMOps, and GenAI governance. Most builders can wire up a prompt; what sets my work apart is the engineering discipline around it, I treat prompts and agents as software: versioned, tested, and gated by real evaluation (LangSmith + DeepEval) before anything ships.
You get a multi-agent workflow built with LangGraph and Claude, deployed on Google Cloud (Vertex AI) or Microsoft Azure, with retrieval, guardrails, PII handling, and audit trails where you need them. Every technical decision is tied to a business outcome — speed, quality, and cost-to-serve.
I've architected enterprise-grade agentic platforms for regulated environments (see the Atlas case study in my portfolio). Whether you're rescuing a stalled pilot or starting from scratch, I'll help you ship something measurable and defensible.
I'm a senior AI architect specializing in agentic systems, LLMOps, and GenAI governance. Most builders can wire up a prompt; what sets my work apart is the engineering discipline around it, I treat prompts and agents as software: versioned, tested, and gated by real evaluation (LangSmith + DeepEval) before anything ships.
You get a multi-agent workflow built with LangGraph and Claude, deployed on Google Cloud (Vertex AI) or Microsoft Azure, with retrieval, guardrails, PII handling, and audit trails where you need them. Every technical decision is tied to a business outcome — speed, quality, and cost-to-serve.
I've architected enterprise-grade agentic platforms for regulated environments (see the Atlas case study in my portfolio). Whether you're rescuing a stalled pilot or starting from scratch, I'll help you ship something measurable and defensible.
AI Development Type
Deep Learning, Knowledge Representation, Model Tuning, Recommendation SystemAI Tools
Amazon SageMaker, Azure Machine Learning, Chainer, Google AutoML, MLflow, NVIDIA AI Platform, PyBrainAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$100
|
Standard
$150
|
Advanced
$250
|
|---|---|---|---|
| Delivery Time | 4 days | 10 days | 15 days |
Number of Revisions | 2 | 3 | 5 |
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
+$50 - $100About Rahul
Enterprise AI Engineer & Architect | LangGraph, LLMOps & Voice Agents
Bengaluru, India - 6:19 am local time
If you are struggling with orchestration bottlenecks, high latency in voice agents, or RAG semantic drift, I design and build production-grade, business-first Agentic architectures that scale reliably.
With over 13 years of industry experience in predictive intelligence and deep learning, backed by 2.5 years of deep production experience in Agentic workflows, I bridge the gap between high-level business logic and robust LLMOps.
Here is exactly how I help engineering and product teams:
1. Multi-Agent Orchestration & Core Workflows
I build stateful, resilient multi-agent systems using LangGraph, LangChain, and CrewAI. Whether you are transitioning away from brittle sequential graphs or building complex reasoning loops with advanced tooling, I design frameworks that maintain state and handle parallel branches flawlessly.
2. Advanced LLMOps, Reliability & Evaluation
I ensure your production models are grounded, compliant, and accurate by designing:
• Rigorous evaluation frameworks & custom prompt registries.
• Prompt optimization systems utilizing DSPy and TextGrad.
• High-performance retrieval pipelines (Advanced RAG) optimized across Pinecone, Qdrant, Weaviate, and Pgvector.
3. Production Infrastructure & Real-World Serving
I build with low latency, strict data privacy, and token cost optimization in mind:
• High-throughput LLM serving using vLLM, Ray, and TorchServe.
• Fine-tuning and deployment pipelines via AWS Bedrock, SageMaker, and Azure AI Foundry.
• Seamless automation & CRM integrations utilizing Zapier, Make, and enterprise platforms.
4. Low-Latency Voice Agents (STT/TTS)
I architect context-aware inbound and outbound voice agents optimized for minimal turnaround times using Deepgram, ElevenLabs, and Vapi AI.
Let’s build an AI architecture that is genuinely maintainable, scalable, and built for your business objectives.
Click the "Invite" button to schedule a discovery call, and let’s discuss your system design roadmap.
Steps for completing your project
After purchasing the project, send requirements so Rahul can start the project.
Delivery time starts when Rahul receives requirements from you.
Rahul works on your project following the steps below.
Revisions may occur after the delivery date.
Discovery to Production ready development
Discovery call & requirements review Target architecture & evaluation plan, shared for your sign-off Build the core agent workflow with retrieval Deploy to your GCP / Azure environment & test against the eval set Walkthrough, documentation & handover