Production-Ready AI & Software Engineer

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Preventing Arrow Errors in Diagrams with Mermaid Syntax Guidance - Preventing Arrow Errors in Diagrams with Mermaid Syntax Guidance

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Overview

I am Robi Dany Riupassa, a software engineer and AI specialist with proven expertise in building and deploying production-grade systems with comprehensive documentation, scalable infrastructure, and advanced AI/ML capabilities.

My work demonstrates:

  • Production deployment expertise (Docker, Kubernetes, CI/CD)
  • Multimodal AI capabilities (text, audio, video, image processing)
  • Infrastructure orchestration (MinIO, Kafka, PostgreSQL, Redis, MongoDB)
  • Comprehensive system documentation with architectural diagrams
  • MLOps pipelines and automated model deployment
  • Event-driven architectures and real-time data processing

Enterprise Production Systems

Media Platform - Advertising Context Protocol (AdCP)

A production-ready comprehensive media platform with AdCP integration for OTT streaming, broadcast management, content management, media asset management, and out-of-home advertising systems.

Production Capabilities:

graph LR
    subgraph Storage["Storage and Delivery"]
        MinIO["MinIO S3<br/>Multi-bucket"]
        CDN["CDN<br/>Global delivery"]
        Kafka["Kafka<br/>Event streaming"]
    end

    subgraph AI["Multimodal AI Extraction"]
        MetaOrch["Metadata<br/>Orchestrator"]
        NLPSvc["NLP Service<br/>Text analysis"]
        VisionSvc["Vision Service<br/>CLIP + Detection"]
        AudioSvc["Audio Service<br/>Whisper + Diarization"]
        FeatureStore["Feature Store<br/>pgvector"]
    end

    subgraph Core["Core Services - NestJS"]
        OTT["OTT Platform<br/>Next.js + Video.js"]
        BMS["Broadcast Mgmt<br/>GEN21 BMS"]
        CMS["Content Mgmt<br/>Strapi v4"]
        MAM["Media Assets<br/>AI search"]
        Recommendation["Recommendations<br/>3 ML strategies"]
    end

    subgraph AdCP["AdCP Integration"]
        Gateway["AdCP<br/>Gateway"]
        MediaBuy["Media Buy<br/>Protocol"]
        Creative["Creative<br/>Protocol"]
        Signals["Signals<br/>Protocol"]
    end

    CDN --> MinIO
    MinIO -->|S3 Events| Kafka
    Kafka --> MetaOrch
    MetaOrch --> NLPSvc
    MetaOrch --> VisionSvc
    MetaOrch --> AudioSvc
    NLPSvc --> FeatureStore
    VisionSvc --> FeatureStore
    AudioSvc --> FeatureStore

    FeatureStore --> OTT
    FeatureStore --> MAM
    FeatureStore --> Gateway

    CMS --> OTT
    CMS --> BMS
    CMS --> MAM

    OTT --> Gateway
    BMS --> Gateway
    MAM --> Gateway

    Gateway --> MediaBuy
    Gateway --> Creative
    Gateway --> Signals

    classDef infraStyle fill:#FF9800,stroke:#E65100,stroke-width:2px
    classDef aiStyle fill:#9C27B0,stroke:#6A1B9A,stroke-width:2px
    classDef coreStyle fill:#2196F3,stroke:#1565C0,stroke-width:2px
    classDef adcpStyle fill:#4CAF50,stroke:#2E7D32,stroke-width:2px

    class MinIO,CDN,Kafka infraStyle
    class MetaOrch,NLPSvc,VisionSvc,AudioSvc,FeatureStore aiStyle
    class OTT,BMS,CMS,MAM,Recommendation coreStyle
    class Gateway,MediaBuy,Creative,Signals adcpStyle

Technology Stack:

Category Technologies
Backend Services NestJS microservices, TypeScript, Node.js 22
Frontend Next.js 14, React 18, Video.js player, Server-Side Rendering
Databases PostgreSQL 16 + pgvector, Redis 7
Content Management Strapi v4 (headless CMS) - single source of truth
Event Streaming Apache Kafka + Kafka UI
Storage & CDN MinIO S3-compatible storage, Cloudflare R2 / BunnyCDN
AI/ML - Multimodal NLP: spaCy, Hugging Face Transformers
Vision: CLIP embeddings, scene detection, object recognition
Audio: Whisper transcription, speaker diarization
Vector Search: pgvector for semantic similarity
Media Processing FFmpeg (video frame extraction, audio extraction, metadata)
Containerization Docker Compose, multi-stage builds
Orchestration Kubernetes manifests (deployments, services, ingress)
IaC Terraform configurations
CI/CD GitHub Actions workflows
Monitoring Prometheus + Grafana

Key Features:

  • Event-Driven Architecture: S3 events trigger automatic metadata extraction pipeline via Kafka
  • Multimodal AI Enrichment: Every uploaded content automatically enriched with NLP, vision, and audio features
  • Feature Store: Centralized repository with pgvector for semantic similarity search across all content
  • Semantic Search: AI-powered content discovery using vector embeddings
  • Real-time Streaming: OTT platform with adaptive bitrate streaming
  • Scalable Infrastructure: Microservices architecture with independent scaling
  • Production Deployment: Complete Docker Compose setup, Kubernetes-ready

Documentation:

  • Comprehensive architecture documentation (95KB+)
  • Detailed service specifications for all 8 modules
  • Mermaid architectural diagrams
  • API specifications (OpenAPI 3.0)
  • Deployment guides and infrastructure setup
  • Technology decision documents

AI-Powered Help & QnA System

A production-ready enterprise-grade RAG (Retrieval-Augmented Generation) system with free-tier LLM APIs, comprehensive hybrid retrieval, and voice assistant capabilities.

Production Architecture:

graph TB
    subgraph "Client Layer"
        WebUI["React Web Interface"]
        VoiceUI["GenTalk Voice Assistant - Audio recording and playback"]
    end

    subgraph "API Layer - FastAPI"
        API["REST API - Health checks and monitoring"]
        Session["Session Management"]
    end

    subgraph "RAG Orchestration"
        RAG["RAG Orchestrator - Query processing"]
        Router["Query Router - System detection BMS or CMS"]
    end

    subgraph "Retrieval Layer"
        Dense["Dense Vector Search - pgvector HNSW indexing"]
        Sparse["Sparse BM25 Search - pg_trgm native Postgres"]
        RRF["RRF Fusion - Hybrid retrieval"]
        Rerank["BGE Reranker Large - Cross-encoder scoring"]
    end

    subgraph "LLM Layer"
        Primary["Cerebras Llama 70B - 1M tokens per day"]
        Secondary["Gemini 2 Flash - 1500K tokens per day"]
        Tertiary["DeepSeek R1 - Complex reasoning"]
        Fallback["Groq or OpenRouter - Automatic fallback"]
    end

    subgraph "Storage Layer"
        PG["PostgreSQL 16 with pgvector - 40K doc chunks indexed"]
        Redis["Redis 7 - Response caching 75 percent hit rate"]
        Embed["BGE-large-en - 1024-dim embeddings"]
    end

    WebUI --> API
    VoiceUI --> API
    API --> Session
    Session --> RAG
    RAG --> Router
    Router --> Dense
    Router --> Sparse
    Dense --> RRF
    Sparse --> RRF
    RRF --> Rerank
    Rerank --> Primary
    Primary -.->|Fallback| Secondary
    Secondary -.->|Fallback| Tertiary
    Tertiary -.->|Fallback| Fallback

    Primary --> Redis
    Dense --> PG
    Sparse --> PG
    PG --> Embed

    classDef clientStyle fill:#E1F5FE,stroke:#01579B,stroke-width:2px
    classDef apiStyle fill:#FFF9C4,stroke:#F57F17,stroke-width:2px
    classDef retrievalStyle fill:#F3E5F5,stroke:#4A148C,stroke-width:2px
    classDef llmStyle fill:#E8F5E9,stroke:#1B5E20,stroke-width:2px
    classDef storageStyle fill:#FFE0B2,stroke:#E65100,stroke-width:2px

    class WebUI,VoiceUI clientStyle
    class API,Session,RAG,Router apiStyle
    class Dense,Sparse,RRF,Rerank retrievalStyle
    class Primary,Secondary,Tertiary,Fallback llmStyle
    class PG,Redis,Embed storageStyle

Technology Stack:

Category Technologies
Backend FastAPI, Python 3.11+, Uvicorn, Pydantic
Frontend React 18, Vite, Axios
Database PostgreSQL 16 + pgvector extension
Caching Redis 7 (75%+ cache hit rate)
Embeddings BGE-large-en-v1.5 (1024-dim, MIT license)
Reranking BGE-reranker-large (cross-encoder)
LLM Providers Cerebras (Llama 70B), Google Gemini 2.0 Flash, DeepSeek R1, Groq, OpenRouter
RAG Framework Custom RAG orchestrator with hybrid retrieval
Voice Processing GenTalk voice assistant with audio recording
Containerization Docker Compose
Orchestration Kubernetes manifests (StatefulSet, ConfigMap, Secrets)
Testing Pytest with coverage reporting
Observability Health checks, cache stats, token usage tracking

Key Features:

  • Hybrid Retrieval: Dense vector search (pgvector) + Sparse BM25 (pg_trgm) with RRF fusion
  • Multi-Provider LLM Fallback: Automatic failover across 5 free-tier providers
  • Voice Assistant: GenTalk with audio recording and playback capabilities
  • System Detection: Automatic BMS/CMS detection with manual override
  • Real-time Citations: Transparent source attribution for all answers
  • Production-Grade Caching: Redis-based response caching (75%+ cost reduction)
  • Comprehensive Documentation: 40K+ lines of real documentation indexed
  • Quality Metrics: Accuracy ≥75%, Citation coverage ≥90%, Hallucination rate ≤5%

Performance Benchmarks:

  • API Health Check: <50ms
  • Embedding Generation: 100-200ms
  • Vector Search: 50-100ms
  • End-to-end RAG: 2-5s
  • Cache Hit Rate: 35-50%

Broadcast Analytics System - MLOps Platform

A production-grade enterprise MLOps platform for broadcast analytics with AI-powered insights, predictive modeling, schedule optimization, and intelligent LLM chat interface.

Production MLOps Pipeline:

graph LR
    subgraph Data["Data Layer"]
        PG["PostgreSQL 16<br/>ML Registry"]
        Mongo["MongoDB 7<br/>Chat History"]
        Redis["Redis 7<br/>LLM Cache"]
    end

    subgraph ML["ML Pipeline"]
        Loader["Data<br/>Loader"]
        Preprocess["Feature<br/>Engineering"]
        Training["Model<br/>Training"]
        Evaluation["Model<br/>Evaluation"]
        Registry["ML<br/>Registry"]
    end

    subgraph LLM["LLM Agent"]
        Agent["LangChain<br/>Agent"]
        LLMPrimary["Groq<br/>Llama 70B"]
        LLMSecondary["Gemini<br/>2 Flash"]
        LLMFallback["OpenRouter<br/>Fallback"]
        Cache["Response<br/>Cache"]
    end

    subgraph API["API Layer"]
        APINode["REST API<br/>OpenAPI"]
        Predictor["Prediction<br/>Service"]
        Optimizer["Schedule<br/>Optimizer"]
        Export["Data<br/>Export"]
    end

    subgraph UI["Frontend"]
        Dashboard["Monitoring<br/>Dashboard"]
        Chat["LLM Chat<br/>Interface"]
        Charts["Interactive<br/>Charts"]
    end

    subgraph Monitor["CICD and Monitoring"]
        GHA["GitHub<br/>Actions"]
        Prometheus["Prometheus<br/>Metrics"]
        Grafana["Grafana<br/>Dashboards"]
        Alertmanager["Alert<br/>Manager"]
    end

    Loader --> Preprocess
    Preprocess --> Training
    Training --> Evaluation
    Evaluation --> Registry
    Registry --> PG

    APINode --> Predictor
    APINode --> Optimizer
    APINode --> Export
    Predictor --> Registry

    Agent --> LLMPrimary
    LLMPrimary -.->|Fallback| LLMSecondary
    LLMSecondary -.->|Fallback| LLMFallback
    Agent --> Cache
    Cache --> Redis
    Agent --> Mongo

    Dashboard --> APINode
    Chat --> Agent
    Charts --> APINode

    APINode --> Prometheus
    Prometheus --> Grafana
    Prometheus --> Alertmanager

    classDef dataStyle fill:#FFE0B2,stroke:#E65100,stroke-width:2px
    classDef mlStyle fill:#E1BEE7,stroke:#4A148C,stroke-width:2px
    classDef llmStyle fill:#C8E6C9,stroke:#1B5E20,stroke-width:2px
    classDef apiStyle fill:#BBDEFB,stroke:#0D47A1,stroke-width:2px
    classDef frontendStyle fill:#F8BBD0,stroke:#880E4F,stroke-width:2px
    classDef cicdStyle fill:#FFF9C4,stroke:#F57F17,stroke-width:2px

    class PG,Mongo,Redis dataStyle
    class Loader,Preprocess,Training,Evaluation,Registry mlStyle
    class Agent,LLMPrimary,LLMSecondary,LLMFallback,Cache llmStyle
    class API,Predictor,Optimizer,Export apiStyle
    class Dashboard,Chat,Charts frontendStyle
    class GHA,Prometheus,Grafana,Alertmanager cicdStyle

Technology Stack:

Category Technologies
Backend FastAPI, Python 3.12+, SQLAlchemy, Motor (async MongoDB)
Frontend React 18, Chart.js, Axios, GEN21 theme
Databases PostgreSQL 16 (ML registry), MongoDB 7.0 (sessions), Redis 7.0 (caching)
ML Frameworks Scikit-learn, XGBoost, CatBoost, Gradient Boosting, MLP, ElasticNet
LLM Framework LangChain agents with 7 specialized tools
LLM Providers Groq (Llama 70B), Google Gemini 2.0 Flash, OpenRouter
Optimization Genetic Algorithm-based schedule optimizer
Containerization Docker Compose, multi-stage Dockerfile
Orchestration Kubernetes (deployments, services, ingress, secrets)
CI/CD GitHub Actions (testing, build, deployment)
Monitoring Prometheus 2.48.0 + Grafana 10.2.2 + Alertmanager
Logging Loki + Promtail for log aggregation
Testing Pytest with coverage reporting

Key Features:

  • Automated ML Pipeline: 24 trained models (6 algorithms × 4 target variables) with auto-selection based on R² > 0.7
  • Model Registry: Database-backed versioned model storage with automated deployment
  • LLM-Powered Analytics: Natural language query system with multi-provider support and automatic fallback
  • Response Caching: Redis-based LLM response cache achieving 75%+ cost reduction
  • Schedule Optimization: Genetic Algorithm-based optimizer with custom constraints and real-time predictions
  • Comprehensive Monitoring: Industrial-grade stack with Prometheus, Grafana dashboards, and automated alerting
  • Multi-Database Architecture: PostgreSQL for structured data, MongoDB for sessions, Redis for caching
  • CI/CD Automation: GitHub Actions for automated testing, building, and deployment
  • Professional Documentation: Complete guides for development, production deployment, and MLOps practices

Performance Metrics:

  • API Latency: 200-500ms average, <2s at P95
  • Database Queries: <100ms
  • Cache Access: <10ms
  • Model Inference: 50-100ms single prediction
  • Schedule Optimization: 2-5 seconds

Recommendation Systems Research & Exploration

A comprehensive research project implementing and comparing YouTube Two-Tower, Netflix Foundation, and Hybrid recommendation architectures. Features interactive exploration apps and systematic Grid Search optimization.

System Architectures Compared:

flowchart LR
    subgraph YT["YOUTUBE: Two-Tower"]
        YT1[User] & YT2[Item] --> YT3[Embed] --> YT4[ANN] --> YT5[Rank]
    end
    subgraph NF["NETFLIX: Foundation"]
        NF1[History] --> NF2[Transformer] --> NF3[Multi-Task] --> NF4[Recs]
    end
    subgraph HY["HYBRID: Combined"]
        HY1[Two-Tower] --> HY2[Transformer] --> HY3[CF] --> HY4[Rank]
    end

    classDef ytStyle fill:#FF6B6B,stroke:#C92A2A
    classDef nfStyle fill:#E50914,stroke:#B20710
    classDef hyStyle fill:#4CAF50,stroke:#2E7D32

    class YT1,YT2,YT3,YT4,YT5 ytStyle
    class NF1,NF2,NF3,NF4 nfStyle
    class HY1,HY2,HY3,HY4 hyStyle

Grid Search Optimization Results:

System Combined Score Precision Diversity Best Configuration
YouTube 0.744 0.860 0.655 Emb: 128, Candidates: 100, K: 5
Hybrid 0.688 0.524 1.000 Candidates: 50, K: 5, CF: 0.2
Netflix 0.625 0.542 0.738 Emb: 64, K: 5, Foundation: 0.3

Technology Stack:

Category Technologies
Deep Learning PyTorch, Transformers, Two-Tower Networks
Vector Search FAISS (Approximate Nearest Neighbor)
Embeddings Sentence Transformers, Custom Embeddings
Interactive UI Gradio Web Applications
Data Processing Pandas, NumPy, Scikit-learn

Key Achievements:

  • 108 Hyperparameter Configurations tested via systematic Grid Search
  • 3 Complete Recommendation Systems built from scratch (YouTube, Netflix, Hybrid)
  • Perfect Diversity Score (1.000) achieved with Hybrid system at K=5
  • Enterprise-Grade Data Models with 50,000+ synthetic interactions
  • Interactive Exploration Apps on ports 7860, 7861, 7862

Key Research Findings:

  • K=5 consistently outperforms K=10 and K=20 across all systems
  • Candidate pool size (50 vs 200) has minimal impact on results
  • 128-dim embeddings provide best precision/diversity balance
  • Hybrid approach achieves maximum diversity through combined architecture

Learn More →

Core Competencies

Infrastructure & DevOps

  • Container Orchestration: Docker Compose, Kubernetes (StatefulSets, Deployments, Services, Ingress)
  • Event Streaming: Apache Kafka for event-driven architectures
  • Storage Solutions: MinIO S3-compatible storage, CDN integration
  • Monitoring & Observability: Prometheus, Grafana, Alertmanager, Loki + Promtail
  • CI/CD Pipelines: GitHub Actions workflows for automated testing and deployment
  • Infrastructure as Code: Terraform configurations

AI & Machine Learning

  • Multimodal AI Processing:
  • Text/NLP: spaCy, Hugging Face Transformers, entity extraction, topic modeling
  • Computer Vision: CLIP embeddings, scene detection, object recognition, video frame analysis
  • Audio Processing: Whisper transcription, speaker diarization, audio feature extraction

  • Vector Search: pgvector for semantic similarity search across modalities

  • MLOps & Model Management:

  • Automated ML pipeline orchestration
  • Model registry with versioning
  • Performance monitoring and drift detection

  • Automated retraining workflows

  • Retrieval-Augmented Generation (RAG):

  • Hybrid retrieval (dense vector + sparse BM25)
  • Cross-encoder reranking
  • Multi-provider LLM fallback chains
  • Production caching strategies

Database Architecture

  • Relational: PostgreSQL 16 with pgvector extension for vector similarity search
  • NoSQL: MongoDB 7.0 for flexible document storage
  • Caching: Redis 7+ for high-performance caching
  • Database Optimization: Indexing strategies, query optimization, connection pooling

Backend Development

  • Frameworks: NestJS (microservices), FastAPI (API development)
  • Languages: TypeScript, Python 3.11+
  • API Design: RESTful APIs, OpenAPI 3.0 specifications
  • Architecture Patterns: Microservices, event-driven architecture, CQRS

Frontend Development

  • Frameworks: Next.js 14 (SSR, SSG), React 18
  • Build Tools: Vite, Webpack
  • State Management: Context API, server components
  • Streaming: Video.js player integration, adaptive bitrate streaming

Professional Experience

Software Developer & AI Specialist

A Leading ICT Solutions Provider | Current Position

Working at a leading ICT solutions provider specializing in: - Media convergence technologies for Media & Broadcast Industry - E-commerce solutions and enterprise applications - AI-powered systems for enhanced enterprise efficiency

Key Achievements:

  • Architected and deployed production-grade media platform with comprehensive AdCP integration
  • Implemented multimodal AI extraction layer processing 40K+ documents with NLP, vision, and audio analysis
  • Built enterprise RAG system with 75%+ cost reduction through intelligent caching
  • Deployed MLOps platform with automated model training, registry, and CI/CD pipelines
  • Created comprehensive documentation (350+ pages) with architectural diagrams and deployment guides

View Full Experience →

Education & Research

Master of Science (MSc) in Physics Bandung Institute of Technology, Indonesia

Learn more about my education →

Research Interests

  • Artificial Intelligence & Machine Learning
  • Computer Vision & Multimodal AI
  • Natural Language Processing
  • Medical Image Analysis
  • Deep Learning Applications
  • IoT & Microcontroller Systems
  • MLOps & Production AI Systems

Personal & Research Projects

In addition to enterprise projects, I maintain a portfolio of personal and research projects:

Browse all projects →

Publications

My research has been published in various scientific journals and conferences.

Learn more about my publications →

Contact

Feel free to reach out for collaborations, consultations, or inquiries about AI, machine learning, and technology solutions.

More contact information →