You will get review your AI architecture, model stack, cost and governance risks
Rising Talent

Rising Talent

Project details
Access constraints and confidentiality requirements.Your AI stack may be producing useful output while still costing too much, creating hidden governance risk, or locking you into the wrong architecture.
I will review your current AI workflow, model/provider choices, architecture, quality signals, costs, latency, and operational controls. You will receive decision-ready recommendations—not a generic checklist.
This service is best for CTOs, product leaders, and operators who need a clear answer to:
Are we using the right AI/model approach?
Where are we overpaying or taking unnecessary risk?
What should we fix first?
What should we build, buy, or stop doing?
You will receive a written findings memo, prioritized next steps, and an optional follow-on roadmap based on the package selected.
I will review your current AI workflow, model/provider choices, architecture, quality signals, costs, latency, and operational controls. You will receive decision-ready recommendations—not a generic checklist.
This service is best for CTOs, product leaders, and operators who need a clear answer to:
Are we using the right AI/model approach?
Where are we overpaying or taking unnecessary risk?
What should we fix first?
What should we build, buy, or stop doing?
You will receive a written findings memo, prioritized next steps, and an optional follow-on roadmap based on the package selected.
AI Algorithms
Autoencoder, Convolutional Neural Network, Generative Adversarial Network, Large Language Model, Recurrent Neural Network, Regression Analysis, Self-Organizing Map, Transformer ModelAI Applications
Time Series ForecastingAI Development Language
PythonAI Tools
Azure OpenAI, GitHub Copilot, Gradio, Hugging Face, NVIDIA AI Platform, PyTorch, Streamlit, TensorFlow, Word2vecAI Models
AlphaCode, ChatGPT, GPT-3, GPT-4, GPT-J, GPT-Neo, LaMDA, LLaMA, Naive Bayes Classifier, OpenAI CodexWhat's included
| Service Tiers |
Starter
$1,500
|
Standard
$4,500
|
Advanced
$8,500
|
|---|---|---|---|
| Delivery Time | 5 days | 10 days | 15 days |
Number of Revisions | 1 | 1 | 1 |
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 Sidharth
AI Engineer | LangGraph | Architecture (UCLA/IIT, Ex Amazon, BofA)
Chandigarh, India - 3:40 am local time
I’ve led software engineering teams at Ansys and Bank of America and worked as a Senior Product Manager at Amazon. My background combines engineering, product, and finance: MS from Rensselaer Polytechnic Institute, MBA from UCLA Anderson, and BTech from IIT Patna.
I’ve built trading pipelines, a customer-retention platform used by 80,000+ brands, and attention-based credit-risk models for my own fintech startup. I understand both the technical work of building AI systems and the business consequences when they fail.
How I can help
- Review AI agent architectures built with LangGraph, LangChain, multi-agent, or deep-agent workflows
- Identify weaknesses in authentication, authorization, state management, tool access, reliability, observability, and failure recovery
- Debug inconsistent, brittle, or unreliable LLM and agent behavior
- Strengthen production AI workflows so they can handle real users, edge cases, retries, partial failures, and scale
- Review code and system design with clear priorities—not generic best-practice checklists
My reviews are structured around critical, should-fix, and nice-to-have findings. Every issue includes a concrete recommendation, why it matters, and a practical path to resolve it.
Most engagements begin with a short conversation about what has already gone wrong: unreliable outputs, stuck workflows, unexpected costs, security concerns, production incidents, or a prototype that is no longer sufficient. Those symptoms usually reveal where the highest-value work is.
I’m LangChain-certified in agentic engineering, with hands-on LangGraph experience. While I can support broader AI product and architecture decisions as an engagement evolves, I prefer to start with a specific, shippable problem and deliver useful progress quickly.
Steps for completing your project
After purchasing the project, send requirements so Sidharth can start the project.
Delivery time starts when Sidharth receives requirements from you.
Sidharth works on your project following the steps below.
Revisions may occur after the delivery date.
Agree on an operating agreement and compliance requirements
Gather system operating parameters and implementation details