Ivan Sazontov | Verified AI Builder

About Ivan

Machine Learning Engineer · AI Engineer

3+ years of production experience building end-to-end ML systems. Built and deployed RAG systems (8,361 chunks, FAISS, local LLM, FastAPI, Docker), real-time computer vision (YOLO + ResNet, 94% mAP), and MLOps APIs (23 endpoints, auto-retraining, auth).

Master's in Applied Mathematics. Several publications in computer vision. Strong in Python, PyTorch, FastAPI, Docker, SQL.

Currently based in Vietnam. Open to remote or local roles. Looking to build AI products that actually work in production — agents, RAG, automation, and scalable ML systems.

Portfolio: greencat1.tech

GitHub: github.com/greencat1

Professional profile

Location: Vietnam

Proven on the network

Implementation records and publicly available work, where published.

3 AI Deployments delivered, including 3 publicly identified implementations available to inspect.

Reported business outcomes

Hours Automated9 hours
period not specified
Automated 9 hours (period not specified)

Builder-reported outcomes, recorded on deployment and case study records that AI Deploy Network reviewed before publication. AI Deploy Network does not currently capture a measurement basis or calculation methodology for reported outcomes, so none is shown.

What Ivan builds

Implementation domains listed on this profile. Evidence-supported items are identified separately below.

Specialisations

AI Agents

Builder-listed deployment types

AI Agent
Computer Vision
Knowledge Assistant
Process Automation
RAG System

AI Deployments

Real AI implementations demonstrated through evidence.

01 / AI DeploymentPublished record

Production MLOps solution for customer churn prediction

End-to-end churn prediction service with 23 REST API endpoints, auto-retraining, manual labeling, and role-based access contro

3 hours automated

Telecommunications · Process Automation

View Deployment

02 / AI DeploymentPublished record

Traffic Sign Detection (CV)

Real-time traffic sign recognition system for 55 classes, deployed via WebSocket with browser camera

2 hours automated

Logistics & Transportation · Computer Vision

View Deployment

03 / AI DeploymentPublished record

RAG-powered portfolio assistant for technical documentation

A fully local RAG system that answers questions about technical portfolios, codebases, and documentation.

4 hours automated

Education · AI Agent · Knowledge Assistant · RAG System

View Deployment

Implementation capabilities

Evidence-supported means linked to a published Deployment or Case Study; other capabilities are Builder declared.

Evidence-supported

AI Agent2 records
Computer Vision1 record
Knowledge Assistant2 records
Process Automation1 record
RAG System2 records

Builder declared

AI Agents

Technology

Technologies listed by the Builder, distinguished by published evidence.

Evidence-supported

Docker3 records
FAISS1 record
FastAPI3 records
Ollama1 record
PyTorch1 record
Python3 records

Builder declared

Chroma
LangChain
LangGraph
OpenAI Platform
Pandas
PostgreSQL
TensorFlow

Industry experience

Published Deployments support the listed counts. Other industries are Builder declared.

Education

1 published Deployment

Logistics & Transportation

1 published Deployment

Telecommunications

1 published Deployment

Cross-Industry / Horizontal

Builder declared

Data & Analytics

Builder declared

Digital Health

Builder declared

FinTech

Builder declared

Case Studies

Published narratives of implementation and results.

Education · AI Agent · Knowledge Assistant · RAG System

RAG-powered portfolio assistant for technical documentation

A fully local RAG system that answers questions about technical portfolios, codebases, and documentation — all running locally with no cloud dependencies.

Read case study

Trust & verification

A closer look at how this profile and its evidence are reviewed.

Verified Builder

AI Case Studies verified

Published evidence is reviewed against the AI Deploy Network Evidence Standard.

How verification works

Verified

Verified Builder

Independently reviewed by AI Deploy Labs against the platform Evidence Standard.

AI Case Studies Verified

How this is verifiedWhat the platform has established, and what remains builder-declared.

Identity and organisation: Identity checks confirm that the person presenting this profile controls the account and the professional identifiers attached to it, such as a LinkedIn profile. Organisation membership is confirmed by the organisation, not self-declared by the builder.
AI Deployments: An approved AI Deployment is an implementation record the builder submitted and the platform reviewed against the Evidence Standard before publication. Approval confirms the record is complete, coherent and consistent with the supporting material provided — it is not an audit of the client's systems.
Business outcomes: Outcome figures are recorded by the builder on the deployment or case study that produced them, and are reviewed at publication. Aggregates shown on this profile are calculated from those approved records only.
Evidence: Supporting evidence comes from the records the builder published on this platform: approved AI Deployments, published AI Case Studies, verification outcomes and organisation participation.
Currency: Older evidence continues to count and is never removed. Recency is presented separately through Professional Currency so that current expertise can be assessed alongside historical work.
Traceability: Every figure on this profile can be followed to the record that produced it. Open the linked AI Deployment or AI Case Study to read the outcome exactly as it was reported.

The platform does not independently audit client systems or financial records. How this is verified

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