Sinejan Eser
Cloud/DevOps & AI Engineer
Summary
Cloud/DevOps & AI Engineer focused on designing scalable, efficient, and reliable cloud systems. Experienced in applying DevOps practices to improve automation and system performance, with an active interest in integrating AI-driven solutions into modern architectures. Currently building production-grade infrastructure at Sufle (AWS Advanced Tier Services Partner) and co-founding myCVpath as a fullstack developer.
Technical Registry
AI/ML
Cloud and DevOps
Monitoring and Observability
Data and Systems
Architecture & Strategy
Programming and Development
Foundations
İstanbul Aydın University
Bachelor of Science in Computer Engineering
2021 – 2026- GPA: 3.32/4
- Honours: 2022-2023 Fall & Spring, 2023-2024 Summer & Spring, 2024-2025 Fall, 2025-2026 Fall
Verified
AWS Certified Machine Learning Engineer – Associate
Amazon Web Services Training and Certification
AWS Certified Developer – Associate
Amazon Web Services Training and Certification
AWS Certified Cloud Practitioner
Amazon Web Services Training and Certification
AWS Certified AI Practitioner
Amazon Web Services Training and Certification
Endorsements
Experience
Sufle
02/2026 – Present- 01
Provisioned and managed scalable AWS infrastructure using Terraform and Terragrunt, supporting production-grade and data-intensive workloads across hybrid cloud environments
- 02
Automated engineering workflows and data pipeline operations by developing Python/Shell-based tooling, reducing manual overhead and improving operational efficiency
- 03
Collaborated with cross-functional teams to troubleshoot production issues and provide actionable insights for system optimization
- 04
Dockerized applications and deployed them on Amazon ECS, integrating CI/CD pipelines to enable automated, consistent, and scalable release processes
- 05
Provided AI consulting using Amazon Bedrock to integrate LLM-powered automation workflows
myCVpath
11/2025 – Present- 01
Architected and integrated a scalable, modular SaaS application using a manager-pattern architecture to enforce separation of concerns across frontend and backend layers
- 02
Deployed and managed the application on-premises using Docker and Docker Compose, implementing containerized environments for scalable, maintainable, and reproducible deployments
- 03
Integrated AI/LLM powered features to automate workflows, enable real-time feedback, and enhance overall user experience
- 04
Designed and implemented core CV rendering, editing, validation, and PDF generation workflows, ensuring consistent behavior across preview and tailoring modes
- 05
Engineered complex, state-aware multi-step workflows with secure navigation guards, robust error handling, and unified user/guest identity management, thoroughly tested using automated scripts
- 06
Built a reusable, accessible shared component library with React, Next.js, and TypeScript to accelerate development and ensure UI consistency
Skyloop
01/2025 – 12/2025- 01
Managed and supported cloud infrastructure using AWS managed services, optimizing deployments, automating configurations, and implementing Infrastructure as Code to streamline operations
- 02
Executed CI/CD pipelines, monitored cloud systems using advanced observability and monitoring tools, and resolved complex issues to accelerate development and deployment processes
- 03
Worked with Amazon EKS in production, managing containerized workloads, HPA-based auto-scaling, and monitoring systems to maintain high availability and resource efficiency
- 04
Migrated production environments from on-premises to AWS with 0 downtime, successfully transitioning 100% of critical workloads while maintaining service availability and operational continuity
- 05
Developed and deployed AI-driven solutions, including Python-based backend services for custom agentic chatbots and intelligent document processing pipelines
- 06
Trained anomaly detection models using Python for a major telecommunications company and presented results to leadership, contributing to strategic decision-making in cloud operations
Peace-Keepers.io
06/2024 – 08/2024- 01
Conducted cloud resource discovery and inventory analysis for Azure services, documenting system architecture, dependencies, configurations, and migration readiness gaps to support a transition to AWS
- 02
Collaborated with stakeholders to gather technical and business requirements, contributing to Total Cost of Ownership (TCO) modeling and business case development
- 03
Supported the creation of a cloud migration strategy and delivered a comprehensive readiness assessment report outlining risks, recommendations, and next steps
Peace-Keepers.io
12/2023 – 01/2024- 01
Utilized Python for data cleaning and exploratory data analysis (EDA), and created Tableau visualizations and dashboards to present findings to the team
- 02
Gained hands-on experience with data analytics workflows, including data preparation, analysis, and insight reporting
Community
Amazon Web Services (AWS) Student Club Istanbul
04/2024 – 02/2025- Led community growth initiatives to scale the AWS Student Club, resulting in a 50% increase in active member engagement
- Organized and delivered AWS-focused conferences, workshops, and hands-on labs to 100+ participants, improving practical cloud knowledge and hands-on skills
- Collaborated with AWS Solution Architects to design and host high-impact technical sessions, strengthening industry-aligned learning outcomes
Projects
Verified professional contributions that power the engineering dossier.
MyCVPath — AI-Native CV Intelligence Platform
Production-deployed polyglot microservices platform automating the full CV-to-job-application lifecycle using a 6-agent LLM orchestration pipeline, ATS-optimized PDF rendering, Rust telemetry sink, and real-time admin control plane. Built across five independently deployable services (Go, Python, Rust, Node.js, Next.js) sharing a PostgreSQL backend with dual-schema architecture. Features tiered billing with BYOK API key encryption, atomic guest-to-user migration, and job-scoped immutable CV snapshots. Live at mycvpath.com.
AWS SageMaker RCF Multi-Server Anomaly Detection
Enterprise-grade anomaly detection system for a telecom client monitoring 500+ servers using Amazon SageMaker's Random Cut Forest algorithm. Detects abnormal CPU, memory, and network I/O patterns via an event-driven Lambda inference pipeline with automated retraining. Replaced static threshold alerting, reducing mean time to detection from 1 hour to 4.3 seconds.
EKS Deployment and Monitoring Project
Production Kubernetes infrastructure automating the deployment and monitoring of n8n workflow automation, Open WebUI, and a full observability stack (Prometheus, Grafana, OpenTelemetry) on a 20-node AWS EKS cluster. Provisioned entirely via Terraform with zero manual console actions. Reduced incident MTTR from 45 minutes to under 8 minutes and achieved 99.9% uptime over a 6-week production period.
On-Premise to AWS Migration
Migrated a production Laravel application, MySQL database (11.2M rows, 28 GB), and 3 TB of frontend assets from on-premise Ubuntu servers to AWS with zero reported downtime. Used a Replatform approach for MySQL (on-premise → RDS Multi-AZ) and Rehost with Modernization for the app layer (bare-metal → EC2 Auto Scaling Group with AMI baking). DNS-level cutover executed in under 3 minutes during a low-traffic window after 48 hours of parallel validation.
AI-Powered HR Analytics System
Conversational analytics platform enabling Turkish-speaking HR teams to query MongoDB HR data in natural language. Built on Amazon Bedrock with Claude 3.5 Sonnet, the dual-engine system routes simple queries through a deterministic local engine and complex ones through LLM-generated MongoDB aggregation pipelines. Achieved 85% first-attempt success rate and reduced report generation from 2–3 days to under 8 seconds.
Venue Discovery Chatbot with AWS Bedrock
Turkish natural language chatbot for a venue booking platform that extracts structured search parameters (location, amenity tags, guest capacity) from free-text queries using Claude v2 via Amazon Bedrock. Achieved 90% parameter extraction accuracy and reduced downstream parsing errors from 20% to under 1% through schema-strict JSON output. Users showed 30% higher venue discovery engagement vs the traditional filter form in A/B testing.