Damian Radinoiu
Damian Radinoiu

Damian Radinoiu

AI/ML • Intern • 29 years • Bucharest, Romania • UTC+02:00 • Work permits: Romania • Available in 2 weeks
Python Developer
8+ years exp
Python
SQL
Active 10 hours ago
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Overview
Technical skills
Timeline
Roles

Overview

An AI-focused software engineer at an intern-level with a stated interest in LLM systems and cloud-based retrieval. The strongest claimed capability appears to be LLM systems and RAG as described in the profile text, but there are no human-authored code artifacts that prove this claim. There is no public human-authored source code, tests, experiment logs or deployment artifacts to evaluate engineering depth or ML-specific skills.

Technical skills

Python• Senior • 8y+
SQL• Middle • 4y+
Python
FastAPI
Asyncio• 4y+
Pydantic
SQLAlchemy
Databases
PostgreSQL• 4y+
AI/ML
Prompt Engineering
AI/ML
LLM Apps
Claude
LLM
RAG
Semantic Search
DevOps
AWS
Azure
GCP
Cloud
CI/CD• 4y+
Docker• 4y+
Rest API• 4y+
Kubernetes
Vector

Timeline

Senior Full Stack Engineer (Python/AI) • Senior
Braithwate • Full-Time
Apr 2026 to Present 4 Months Bucharest Partially remote
Built an AI-driven regulatory intelligence platform using Python and FastAPI with structured validation and database-backed persistence. Developed an agentic assistant that performs multi-step retrieval and generates citation-grounded responses from regulatory sources. Implemented LLM-assisted extraction and document enrichment, and designed retrieval pipelines combining deterministic ranking with semantic/vector search. Improved reliability and scalability by separating enrichment from stateless search services and adding automated tests for the retrieval and processing flows.
Python
Asyncio
Pydantic
SQLAlchemy
Rest API
PostgreSQL
Claude
LLM
RAG
Semantic Search
Vector
Senior Backend / Platform Engineer • Senior
Digitain • Full-Time
Mar 2024 to Feb 2026 1 Year 11 Months In office
Delivered backend functionality for high-traffic online games, supporting real-time gameplay and transaction-heavy workflows. Designed platform architecture to deploy multiple FastGames independently using microfrontends and improved modularity for releases. Optimized database stored procedures and data-processing workflows to reduce latency under concurrent load, and modernized services for better maintainability and compatibility. Worked across teams to integrate promotional systems and upgrade legacy components, including refactoring integrations with newer SDKs and updated interfaces.
Python
PostgreSQL
SQL
Asyncio
Rest API
Docker
Kubernetes
CI/CD
Senior Backend Engineer • Senior
Global Teams • Full-Time
Jul 2022 to Feb 2024 1 Year 7 Months In office
Built backend infrastructure for a cryptocurrency trading platform supporting brokerage clients, real-time transactions, and high-throughput financial workflows. Developed and optimized REST APIs for trading operations and account/transaction processing, including integrations with external liquidity and third-party systems. Implemented Python-based automation utilities and designed scalable real-time plus asynchronous data-processing pipelines. Refactored legacy backend components, optimized database access patterns, and supported operational tooling and data-driven dashboards.
Python
PostgreSQLsince 2022
SQLsince 2022
Asynciosince 2022
Rest APIsince 2022
Dockersince 2022
CI/CDsince 2022
Python / Enterprise Automation Engineer • Middle
PureQuad • Full-Time
Oct 2018 to Dec 2020 2 Years 2 Months In office
Developed enterprise software with Python for secure email communication, encryption-related workflows, and automated information-processing. Built internal utilities to process structured/unstructured communication data, validate workflows, and reduce repetitive engineering and support tasks. Integrated Microsoft Outlook APIs to support desktop, Microsoft 365, and web environments, and created automation around diagnostics, log analysis, and troubleshooting. Also built internal geospatial automation tools using Python (ArcPy/ArcGIS SDK) for survey processing, spatial transformations, batch analysis, and reliability improvements in data handling.
Pythonsince 2018
Intern AI/ML Engineer Confidence: Low Generalist
An AI-focused software engineer at an intern-level with a stated interest in LLM systems and cloud-based retrieval. The strongest claimed capability appears to be LLM systems and RAG as described in the profile text, but there are no human-authored code artifacts that prove this claim. There is no public human-authored source code, tests, experiment logs or deployment artifacts to evaluate engineering depth or ML-specific skills.
Model Architecture & Training
How well models are designed and trained
Not evidenced in public code
Data Pipeline & Feature Engineering
How data is prepared for models
Not evidenced in public code
Experimentation & Evaluation
How results are measured and tested
Not evidenced in public code
MLOps & Deployment
How models are shipped to production
Not evidenced in public code
Computational Efficiency
How efficiently computing resources are used
Not evidenced in public code
Research Depth & Innovation
Depth of research and new ideas
Not evidenced in public code
Expertise
Conversational AI & Chatbots• Intern
Industries
Artificial Intelligence• Intern
Technologies
AI/ML
LLM Apps
Cloud
Python• Senior • 8y+
SQL• Middle • 4y+
PostgreSQL• 4y+
Rest API• 4y+
Claude
GCP
SQLAlchemy
FastAPI
Prompt Engineering
Azure
CI/CD• 4y+
AWS
Docker• 4y+
Kubernetes
LLM
RAG
Asyncio• 4y+
Pydantic
Vector
Semantic Search
Recommendations
  • Produce a small, human-authored end-to-end example that demonstrates one concrete system (for example: a RAG-powered search API) including data preprocessing, retrieval index code, and an inference wrapper so reviewers can evaluate architecture and engineering decisions.
  • Add an experiments/evaluation folder with at least one reproducible training or fine-tuning run, a clear metrics report, and minimal experiment tracking (W&B or MLflow) to show experimentation practices.
  • Include basic tests and CI (unit tests for key modules and a GitHub Actions workflow) plus deployment manifests or a lightweight containerized serve example to demonstrate MLOps and production-readiness.
  • Document ownership for major files and mark vendored or template code clearly so reviewers can focus on original work.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories:
hombr33