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Achievements

Achievement: Pull Sharkx3Achievement: Pair ExtraordinaireAchievement: QuickdrawAchievement: YOLO

Achievements

Achievement: Pull Sharkx3Achievement: Pair ExtraordinaireAchievement: QuickdrawAchievement: YOLO

Highlights

  • Pro

Organizations

@Fisor-Analytics

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enesyesil/README.md

Enes Yesil

Platform Engineer - AI, ML & Analytics Infrastructure

Designing and coding the platforms behind AI, ML, and analytics—from backend services and orchestration to governed data systems and observable delivery.

Website · LinkedIn · Email

What I build

I work across software and infrastructure, building the services, APIs, workers, orchestration, and automation that make AI, ML, and analytics platforms reliable.

  • Platform software built with Go, Python, TypeScript, and SQL: backend services, APIs, workers, orchestration, and automation.
  • AI, ML, and Analytics foundations spanning governed data, retrieval, agentic workflows, validation, and controlled access.
  • Operational systems built with infrastructure as code, automated delivery, observability, and resilience patterns.
  • Claude Certified Architect

Production work

At RAVL, I work as a Software Engineer supporting an enterprise analytics platform for a major Canadian financial institution. I build software and automation for Azure Databricks and Unity Catalog platforms, including Terraform modules, GitHub Actions delivery and change-management workflows, Azure networking, storage, identity, and permissions, plus governed integration between on-premises analytics systems and cloud data platforms.

  • Built an AI-assisted knowledge-base workflow for 400+ teams, reducing token usage by 30% and improving RAG retrieval efficiency by up to 20%.
  • Built version-management automation that reduced manual effort by approximately 40%, and consolidated Databricks init-script migrations into a single-step deployment path.
  • Designed governed integration across an on-premises analytics platform, Azure Databricks, and Unity Catalog; automated shared-resource availability and ownership tracking to save approximately 2–3 hours per sprint or feature-development cycle.

Earlier, through York University's BEST program, I built an AI-powered business intelligence platform with Go-based orchestration, distributed Spark and MLlib pipelines, Apache Iceberg, PostgreSQL, pgvector, Redis, natural-language research workflows, and Prometheus observability. I completed my BSc in Computer Science at York University in December 2025.

Selected systems

Personal systems exploring AI orchestration, data processing, and event-driven architecture:

Atlas Fabric

An experimental, LLM-assisted temporal data pipeline for reconstructing historical boundary datasets. A LangGraph generator/reviewer loop combines research and classification with GeoPandas and Shapely processing, deterministic validation, temporal consistency checks, and a FastAPI serving layer.

IssueSight

An event-driven AI platform that turns GitHub issues into architectural context, prerequisite maps, and implementation guidance. Go workers use Redis Streams, distributed locking, caching, PostgreSQL, Next.js, and LLM integration to separate ingestion, processing, and serving.

Secondary work: Fisor Builder, a natural-language-to-structured-dataset research pipeline; and InfraUp, a self-hosted software catalog backed by a Go, Next.js, and PostgreSQL registry stack.

Core toolkit

Data platforms

Azure Databricks Apache Spark Apache Iceberg Unity Catalog Azure Data Lake

AI systems

RAG · agentic workflows · LLM orchestration

LangGraph Claude pgvector

Backend and distributed systems

REST APIs · event-driven architecture

Go Python TypeScript SQL

Platform engineering

Azure AWS Terraform Docker GitHub Actions Linux

Data and state

PostgreSQL Redis MongoDB

Observability

Prometheus Grafana

Current direction

I am continuing to deepen my work in AI and ML infrastructure, enterprise analytics platforms, distributed systems, workload orchestration, data and AI governance, and production observability.

Clear contracts. Governed data. Observable execution.

Pinned Loading

  1. fisor-builder fisor-builder Public

    A research/data pipeline module. It converts open-ended natural language questions into structured datasets

    Python

  2. iceberg iceberg Public

    Forked from apache/iceberg

    Apache Iceberg

    Java

  3. backenderer backenderer Public

    A lightweight, secure, and decentralized backend deploy kit for students and hobbyists. Deploy your backend app to your own cloud account in seconds - no servers to manage, no SSH needed.

    HCL

  4. docufier docufier Public

    A cross-platform desktop app that lets non-technical documentation maintainers easily create, open, and share portable Markdown-based documents.

    JavaScript

  5. issuesight issuesight Public

    IssueSight ingests GitHub issues and uses LLMs to generate "Context Bridges" from breaking down complex issues into junior-level prerequisites, architectural summaries, and implementation guides.

    Go

  6. ckm ckm Public

    A workload orchestration system I built to understand how kernel scheduling, memory management, and production infrastructure come together in real-world systems.

    Go