You will get your RAG pipeline hardened for launch with evals, guardrails, and rollback
Project details
A RAG prototype that answers your test questions is not a launch-ready system. I harden it: I measure and fix retrieval quality against your real queries, enforce grounding and citations, build an eval harness so you know your quality bar, add rate limiting and prompt-injection guardrails, and ship behind a feature flag with a rollback playbook. You launch knowing exactly where it breaks and how to defend it. 15 years in mobile engineering, 6 in production AI — I shipped GPT-4o into Subway's consumer ordering app, used by millions. I treat model output as untrusted until reviewed.
AI Algorithms
Large Language Model, Transformer ModelAI Applications
Conversational AI, Natural Language UnderstandingAI Development Language
PythonAI Models
ChatGPT, GPT-4What's included
| Service Tiers |
Starter
$1,200
|
Standard
$2,800
|
Advanced
$5,500
|
|---|---|---|---|
| Delivery Time | 6 days | 10 days | 15 days |
Number of Revisions | 1 | 2 | 2 |
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 Igor
Agentic RAG + ML/Data Engineer for SaaS & Stripe Systems
Coral Springs, United States - 2:12 am local time
Best fit: funded teams with a broken RAG chatbot, hallucinating AI workflow, weak ML evaluation loop, unreliable data pipeline, or payment/data reconciliation problem that needs a scoped production fix.
Recent paid Upwork proof: Hilltown Media Group funded fixed-price work for a Pretix + Stripe Connect plugin. I shipped destination-charge payment flow work, refund-fee-shield behavior, test coverage, and handoff evidence across M1 + M2. That proof matters because I do not start unfunded work and I ship code/tests, not slideware.
Agentic RAG / ML work I am strongest at:
- RAG evaluation harnesses: retrieval quality, grounding checks, failure cases, regression tests
- Agentic workflow hardening: tool-call gates, budget limits, trace review, rollback paths
- Data Science / ML diagnostics: forecasting/model sanity checks, feature pipelines, dashboard QA
- Production data systems: Python, SQL, TypeScript, BigQuery/GCP, AWS, APIs, observability
- SaaS revenue systems: Stripe Connect, webhooks, payout/reconciliation logic, marketplace flows
Typical fixed-scope starting points:
- $500-$750 AI/data audit with prioritized fixes
- $750-$1,500 RAG or ML evaluation harness
- $1,000-$2,500 data pipeline, dashboard, or Stripe/data reconciliation slice
I am not a good fit for unfunded prototypes, vague "build my whole AI app" requests, or hourly work without a funded milestone. If the scope is real, I will make the risk visible, define the smallest escrow-funded slice, and ship the evidence.
Steps for completing your project
After purchasing the project, send requirements so Igor can start the project.
Delivery time starts when Igor receives requirements from you.
Igor works on your project following the steps below.
Revisions may occur after the delivery date.
Measure, harden, and ship
I measure retrieval quality on your real queries, fix grounding and guardrails, add an eval harness, and ship behind a feature flag with a rollback playbook.