AI / ML Platform
Model lifecycle, progressive delivery, delayed quality feedback, policy-driven release control and observable production operations.
Resume overview
Ömer Faruk Koç
MLOps & AI Platform Engineer — 3+ years building and operating production ML, data and GenAI systems, with current work focused on agent reliability, evaluation, observability and platform control.
View as MarkdownExperience
MLOps & Analytics Engineer
Fibabanka · Analytics Center of Excellence
Built and operated production ML, data and Generative AI platform capabilities across analytics workloads.
- Call-Center IntelligenceGPU-DISTRIBUTED
- ML PlatformEND-TO-END LIFECYCLE
- Data Platform75% RUNTIME REDUCTION
- Feature PlatformOne definition per feature
Featured project
ML Platform
AI Infrastructure / Platform Engineering
A self-hosted Kubernetes ML platform for scheduled scoring, versioned datasets, model monitoring and governed serving, backed by a PostgreSQL control plane and a separate Go inference gateway.
Scheduled scoring and quality
1,000,000 predictions · 100% label coverage
NYC TLC February 2025 · single-node local replay · 8 October 2026
Independent pandas/sklearn checks agree with MAE 2.082 USD, RMSE 4.466 USD and R² 0.920. The illustrative model was trained locally before platform scoring; this is not production traffic or platform-run training.
Core domains
Engineering areas
Generative AI / RAG
Retrieval, reranking, citation integrity, evaluation and private open-source model serving.
Distributed Systems
Event processing with explicit delivery guarantees, failure handling and measured service limits.
Data Engineering
Batch and near-real-time pipelines, transformation systems, quality controls and data lineage.
Agent Systems / Agent Infrastructure
Stateful agent workflows with deterministic control boundaries, confirmation and recovery, secure tool interfaces, evaluation and observability.