The journey: Started as a backend dev, fell in love with AI systems, realized my true calling is building the infrastructure that makes AI work at scale. Not just making models work once—making them work reliably for thousands of users, every single day.

What drives me: There's something magical about building systems where multiple AI models work together seamlessly, where vector searches happen in milliseconds, where failures are handled gracefully. That moment when your platform scales from 100 to 10,000 requests without breaking? That's the high I chase. 🔥

Most people want to fine-tune models. I want to build the platforms where they do it.

Most people focus on one AI model. I focus on orchestrating multiple models with intelligent routing.

Most people build demos. I build production systems engineered for real scale.

The realization: After building my Content Intelligence Platform, I discovered I wasn't just coding—I was solving the hard problems of ML infrastructure: concurrent processing, intelligent caching, multi-model orchestration, production monitoring. This is where backend engineering meets AI innovation. This is my zone.

57K tracks. Multi-model AI pipeline. Production ML platform.

Built a hybrid analysis system combining 4 AI models (LLM, emotion AI, multi-model orchestration) with algorithmic analyzers for optimal speed/cost balance. Smart routing decides: AI when needed, algorithms when faster. Platform thinking in action.

Building an ML platform isn't just about calling an API. It's about:

• Orchestrating hybrid analysis pipeline (4 AI models + algorithmic processors) without conflicts
• Smart model routing deciding between AI depth vs algorithmic speed
• Processing 57K+ records without database locks or timeouts
• Caching intelligently to cut costs by 80% while maintaining freshness
• Monitoring everything because if you can't measure it, you can't improve it
• Handling failures gracefully because production systems fail, and that's okay

🐘 PostgreSQL + pgvector     🔄 20 concurrent connections
🚀 Redis intelligent cache   🎯 85%+ cache hit ratio  
🤖 57K+ tracks analyzed      📊 Hybrid ML pipeline (4 AI + algorithms)
⚡ 50-500ms API response     🧬 RAG + semantic search live
🐳 Docker + K8s ready        📈 25+ custom Prometheus metrics
💰 80%+ cost reduction       🔥 Smart model routing operational

Why these metrics matter: Every number represents a production challenge solved. Connection pooling? Database lock prevention. Cache hit ratio? Cost optimization. Hybrid pipeline? Platform thinking—right tool for the job.

Backend Foundation:

• FastAPI + async/await → Handling concurrent ML workloads without blocking
• PostgreSQL 15 + pgvector → Vector similarity search at scale, no external DB needed
• Redis cache layer → Intelligent deduplication, 1-hour artist TTL, rate limiting state

ML Platform Layer:

• Multi-Model AI Pipeline → Qwen LLM (primary), Emotion AI (HuggingFace models), Multi-model orchestrator, Ollama (local experimentation)
• Algorithmic Processors → Rule-based analysis for 10x faster bulk processing
• Smart Router → Intelligence layer deciding: AI models for complex analysis, algorithms for speed
• RAG Implementation → Semantic search over 57K embeddings, sub-second response times
• LLM Operations → OpenAI integration, prompt engineering, smart caching, model routing
• Cost Optimizer → Redis caching + intelligent routing = 80% cost savings

Production Infrastructure:

• Docker + Kubernetes → Production-ready containerization, scalable deployment
• Prometheus + Grafana → 25+ custom metrics, real-time ML pipeline observability
• Connection Pooling → 20 max concurrent, zero database lock issues
• Chaos Engineering → Fault injection, graceful degradation, resilience testing

Problem 1: Hybrid Pipeline Orchestration

• Challenge: 4 AI models + algorithmic processors need smart coordination, not chaos
• Solution: Intelligent routing layer + async processing + connection pooling + task queuing
• Result: Right tool for each job—AI depth when needed, algorithmic speed for bulk. 20 concurrent analyses, zero conflicts

Problem 2: API Cost Explosion

• Challenge: Every request hitting OpenAI = $$ burning fast
• Solution: Redis-powered intelligent caching with deduplication + smart routing to algorithms when possible
• Result: 80%+ cost reduction, cache hit ratio staying above 85%

Problem 3: Vector Search at Scale

• Challenge: Searching 57K+ embeddings needs to be fast, not just work
• Solution: pgvector + optimized indexing + query optimization
• Result: Sub-second semantic similarity searches

Problem 4: Production Reliability

• Challenge: ML systems fail in creative ways—API timeouts, rate limits, bad data
• Solution: Circuit breakers, retry logic, health checks, chaos testing, graceful model fallback
• Result: System recovers gracefully, never fully crashes. Smart routing adapts when AI APIs are down

Production RAG Systems ✅

• Vector databases (pgvector), semantic search, embeddings at scale
• Hybrid search strategies, recommendation engines
• Real implementation: 57K tracks, sub-second searches

Multi-Model Orchestration ✅

• Hybrid ML pipeline: AI models + algorithmic processors
• Smart routing (complexity-based model selection), cost optimization through intelligent caching
• Real implementation: 4 AI models + algorithmic layer, 80% cost savings through smart routing

Backend for ML ✅

• FastAPI + async Python, PostgreSQL + Redis, connection pooling
• Production patterns: health checks, graceful degradation, monitoring
• Real implementation: 20-connection pool, 85%+ cache hit ratio

ML Infrastructure ✅

• Docker + Kubernetes, Prometheus + Grafana, CI/CD pipelines
• Chaos engineering, resilience testing, observability
• Real implementation: Full monitoring stack, automated deployments

Core Stack:

• 🐍 Python 3.11+ → FastAPI, async/await, Pydantic, pytest
• 🐘 PostgreSQL + pgvector → Vector ops, concurrent access, optimization
• 🚀 Redis → Caching, deduplication, rate limiting, session management
• 🤖 LLM Integration → OpenAI, Anthropic, local models, prompt engineering

ML Platform Tools:

• 🔍 Vector Search → Embeddings, semantic similarity, recommendations
• 🐳 Container Orchestration → Docker, Kubernetes (learning), Helm charts
• 📊 Observability → Prometheus, Grafana, custom metrics, alerting
• 🔧 Chaos Engineering → Fault injection, resilience testing, recovery

Target Role: ML Platform Engineer at companies building AI products at scale

What excites me:

• 🏗️ Building platforms that serve 40+ engineering teams
• 🚀 Scaling ML systems from prototype to production
• 🔧 Solving infrastructure challenges that make AI work reliably
• 📊 Obsessing over metrics that improve system performance
• 🤝 Enabling teams to ship AI features without worrying about infrastructure

What I bring:

• ✅ Real production experience → Not just tutorials, actual systems serving real scale
• ✅ Platform mindset → Multi-model architecture, API-first design, monitoring-first
• ✅ Backend foundation → PostgreSQL, Redis, concurrent processing, enterprise patterns
• ✅ AI integration chops → RAG, vector search, LLM operations at scale
• ✅ Resilience focus → Chaos testing, graceful degradation, production reliability

Not interested in:

• ❌ Research positions (I build platforms, not models)
• ❌ Pure backend roles (I need the ML challenge)
• ❌ Demo-driven projects (production or nothing)

Next 3 Months:

• 🎯 Completing the stack: OpenSearch integration, full Grafana LGTM setup
• 🧪 Chaos engineering: Comprehensive resilience testing suite
• 📊 Advanced features: Feature stores, batch processing optimization
• 💼 Career transition: Actively seeking ML Platform Engineer roles

6-12 Month Vision:

• 🌍 Contributing to platforms serving thousands of users across dozens of teams
• 🚀 Mastering advanced ML infra: Model serving, A/B testing, feature stores
• 🏗️ Platform leadership: Designing scalable AI systems for enterprise
• 🔬 Innovation: Next-gen RAG architectures, multi-modal AI systems

"Build ML platforms that developers love to use."

I combine backend engineering rigor with AI innovation to create systems that scale, monitor, and deliver real business value.

My approach:

• 🎯 Production-first → If it doesn't work under load, it doesn't work
• 🔌 API-driven → Everything has an endpoint, everything is measurable
• 📊 Monitoring-obsessed → You can't improve what you don't measure
• 🧪 Chaos-tested → Break it in staging so it doesn't break in production

The goal: Make AI infrastructure so reliable that teams forget it exists. The best platforms are invisible—they just work.

Looking for ML Platform Engineers? Let's talk about building AI infrastructure together.

Want to discuss RAG architectures, vector databases, or Redis optimization? I'm always down for technical deep dives.

• 📧 Professional: vebohr@gmail.com
• 💬 Telegram: @vastargazing
• 🔗 Project: Content Intelligence Platform

Ready to build ML platforms that serve thousands of users and empower dozens of teams. Let's create AI infrastructure that scales beautifully, caches intelligently, and recovers gracefully. 🚀

Because the future of AI isn't just better models—it's better platforms to run them on.