cyprian@ai-engineer: ~
$|
./view_projects./connect

// AI Engineering Philosophy

python
class AIEngineer:
def __init__(self):
self.approach = "Foundation models + adaptation"
self.focus = "Production-ready AI systems"
self.core_skills = [
"prompt_engineering",
"rag_systems",
"model_finetuning",
"agentic_systems"
]
yaml
ai_engineering_principles:
evaluation_first: "Systematic testing & metrics"
defensive_design: "Security & guardrails"
optimization: "Speed & cost efficiency"
data_quality: "Curated datasets"
user_feedback: "Continuous improvement"
business_impact: "Technical metrics → outcomes"

/* I build production-ready AI systems by leveraging foundation models and adapting them for real-world applications. My focus is on systematic evaluation, defensive engineering, and creating AI products that deliver measurable business impact while maintaining safety and reliability. */

$ cat /proc/ai_engineering_skills

Model Adaptation

▶Prompt Engineering (CoT, System prompts)
▶Parameter-Efficient Fine-tuning (LoRA)
▶Model Selection & Evaluation
▶Behavioral Adaptation

RAG Systems

▶Retrieval Algorithm Design
▶Chunking Strategies
▶Vector Database Integration
▶Hallucination Reduction

Defensive AI

▶Input/Output Guardrails
▶Prompt Injection Defense
▶Safety Evaluation Pipelines
▶Data Leak Prevention

System Optimization

▶Inference Optimization
▶Model Quantization
▶Batching Strategies
▶Cost-Performance Tuning

Agentic Systems

▶Tool-Using Agents
▶Planning & Reasoning
▶Environment Perception
▶Multi-Agent Coordination

Production Engineering

▶MLOps Pipelines
▶A/B Testing for AI
▶Monitoring & Observability
▶Scalable Deployment

$ git log --grep="AI Systems"

a7f3c2d

Mathematical AI Recognition System

production
repo

Production-ready handwriting recognition with real-time inference optimization. Implemented custom prompt engineering for LaTeX conversion and deployed with 99.9% uptime.

AI Engineering Features:

Real-time inferenceCustom promptingError handling
JavaScriptMyScript SDKMathJaxPrompt Engineering
b8e4d1a

Smart Task Management with AI

stable
repo

Full-stack system with AI-powered task prioritization. Implemented RAG for context-aware suggestions and A/B tested different prompt strategies for 23% efficiency gain.

AI Engineering Features:

Context-aware AIRAG implementationA/B testing
ReactFlaskMongoDBRAG Pipeline
c9f5e2b

SmartBudget AI Agent

beta
repo

Agentic system for financial insights using Gemini API. Implemented defensive prompting, spending pattern analysis, and user feedback loops for continuous model improvement.

AI Engineering Features:

Agentic behaviorDefensive promptingFeedback loops
KotlinJetpack ComposeGemini APIAgent Architecture

$ ssh connect@cyprian.dev

/* Ready to build production-ready AI systems? Let's discuss your next AI engineering challenge. */

./github./linkedin./email