ÖFK
Menu

Professional experience

MLOps & Analytics Engineer

Built and operated production ML, data and Generative AI platform capabilities across analytics workloads.

Company
Fibabanka
Team
Analytics Center of Excellence
Period
Mar 2023 — Mar 2026
Location
Istanbul, Türkiye
View as Markdown View full resume
Experience3+ YEARS
Production AI/MLProfessional experience across ML, data and GenAI systems.
Performance75% RUNTIME REDUCTION
Core data workflowSequential Oracle processing was re-architected into modular parallel dbt workflows, reducing end-to-end runtime by 75%.
ProductionEND-TO-END ML
Lifecycle ownershipOwned validation, versioning, promotion, serving, retraining and monitoring across multiple production ML use cases.
InfrastructurePRIVATE GENAI
On-prem GPU servingAdapted validated cloud prototypes to private GPU infrastructure for quantized open-source model serving under data-residency constraints.

Selected impact

Platform work through outcomes.

Stories are organized around engineering changes and measurable effects rather than a conventional responsibility list.

Impact / 01

Call-Center Intelligence

Call recordings contained valuable operational signal but were expensive to inspect manually and difficult to reuse consistently in analytics.

Built a GPU-distributed, high-volume audio intelligence pipeline from transcription and diarization through structured operational outputs.

Processing architecture

GPU-DISTRIBUTED

High-volume daily processing

A GPU-distributed pipeline handled high-volume daily processing end to end: transcription with WhisperX, timestamp alignment, speaker diarization, then LLM summarization into structured fields that feed operational KPIs.

  • WhisperX
  • Timestamp alignment
  • Speaker diarization
  • LLM summarization
  • Structured outputs
  • Operational KPIs

Impact / 02

ML Platform

As production ML use cases expanded, validation, promotion, serving, retraining and monitoring became a shared platform concern.

Owned deployment and lifecycle management across multiple production ML use cases.

Production ML ownership

END-TO-END LIFECYCLE

Multiple analytics use cases

Ownership covered the lifecycle rather than a single handoff: validation, versioning, promotion, serving, retraining and monitoring across multiple analytics use cases.

  • Validation
  • Versioning
  • Promotion
  • Serving
  • Retraining
  • Monitoring

Impact / 03

AI Infrastructure

Generative AI prototypes validated in Azure required data-residency-aware serving and private infrastructure constraints.

Moved validated Azure prototypes to on-premises GPU infrastructure for private open-source model serving.

  • Azure
  • On-prem GPU
  • llama.cpp
  • Ollama
  • LangChain

Impact / 04

Data Platform

As the core Oracle workflow grew, sequential execution increasingly constrained the batch window, dependency visibility and change isolation.

Re-architected sequential Oracle ETL workflows into modular parallel dbt pipelines.

Core ETL impact

75% RUNTIME REDUCTION

Sequential Oracle → modular parallel dbt

End-to-end runtime of the core workflow fell by 75% after splitting monolithic sequential procedures into modular dbt models that execute in parallel.

  • Oracle
  • dbt
  • ETL / ELT
  • Parallel execution

Impact / 05

Feature Platform

Multiple ML use cases independently rebuilt overlapping customer features, creating duplicated maintenance and definition drift.

Built a centralized feature store and dimensional models for reusable production ML features.

Shared feature reuse

One definition per feature

Centralized reuse across production ML use cases

Features and dimensional models were defined once and consumed by the production models, replacing per-model reimplementation of the same customer attributes.

  • Feature store
  • Dimensional modeling
  • Reusable features

Impact / 06

Data Scale

Training, inference and analytics all needed the same customer-level view, assembled from many separate source tables.

Engineered batch and near-real-time training, inference and analytics pipelines.

Customer-level coverage

LARGE-SCALE COVERAGE

Multi-source batch + near-real-time datasets

Batch and near-real-time pipelines consolidate multiple upstream source systems into customer-level datasets for training, inference and analytics.

  • Batch pipelines
  • Near-real-time
  • Training
  • Inference
  • Analytics

Impact / 07

Quality & Monitoring

Without pipeline-level validation, data and model regressions could surface late in downstream analytics.

Implemented data quality, model validation and delivery workflows around production analytics systems.

Failure detection point

Checks in the pipeline, not the report

Data quality + model validation gates

Great Expectations and Deepchecks checks run as part of the Airflow pipelines and GitHub Actions delivery workflow, so data and model regressions are caught where they occur instead of being discovered in downstream analytics.

  • Great Expectations
  • Deepchecks
  • Airflow
  • GitHub Actions

Clear separation

Professional work shaped the engineering problems explored in public projects.

The portfolio projects are independent public engineering work. They are not Fibabanka source code or representations of internal systems.