Zain Ulabidin
Machine Learning Engineer @Machine Learning Reply GmbH
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WORK HISTORY
Machine Learning Engineer @Machine Learning Reply GmbH
Architected distributed analytics pipelines on Cloudera Data Platform, processing over 50 TB of data with sub-second latency. • Developed a unified streaming and batch ingestion framework, enhancing real-time model scoring and training efficiency. • Optimized pipeline runtime by 60% through advanced schema design and tuning, enabling daily refresh of 200+ ML features.
EDUCATION
Stiftung Universität Hildesheim
Master's degree, Data Analytics
University of Management and Technology - UMT
Bachelor of Science (BS), Computer Science
SKILLS
ABOUT ZAIN ULABIDIN
Hands-on technologist who loves turning complex business logic into elegant, scalable software. I architect and build cloud-native web platforms in Python (FastAPI, Django, GraphQL) and run them on AWS using Infrastructure-as-Code (CDK & Terraform). My focus is end-to-end quality: SOLID, DRY, test-driven code; self-healing CI/CD pipelines; observability from the first commit.Core CompetenciesCloud Architecture (AWS) – VPC design, ALB/NLB, RDS & DynamoDB, S3, Lambda, Cognito, Step Functions.Infrastructure as Code – CDK (TypeScript/Python) & Terraform modules powering blue/green & canary deployments.Container & Orchestration – Docker images auto-scaled on ECS Fargate and EKS Kubernetes clusters serving >3 concurrent users with zero-downtime rollouts.Backend Engineering – Fast, type-hinted APIs in FastAPI and robust monoliths in Django, both secured with OAuth2/JWT and instrumented with OpenTelemetry.Data Processing – Batch & streaming pipelines in Apache Spark (PySpark & Scala) orchestrated via Airflow, emitting real-time metrics to Prometheus/Grafana.GraphQL & Front-End – Production-grade GraphQL servers (Ariadne / Strawberry) consumed by React front-ends with Apollo Client.DevOps & Reliability – GitHub Actions, automated canaries, adaptive scaling, synthetic checks, and SLO-driven alerts.Software Craftsmanship – OOP, SOLID design, domain-driven modelling, high-coverage pytest suites, Pydantic/FastAPI-based validation, SQLAlchemy / Django ORM best practices.Selected AchievementsLive analytics platform: Designed a FastAPI + Spark backend on EKS processing 2 TB/day, achieving P95 latency < 350 ms via async I/O and Arrow-based serialization.Self-healing data lake ingestion: Terraform-driven stack with Step Functions & Lambda retries reduced manual ops by 70 %.Global SaaS migration: Lift-and-shift from On Prem to AWS, introducing blue/green Terraform pipelines—cut infra cost 35 % and boosted deployment frequency to daily.Toolchain SnapshotPython • FastAPI • Django • Ariadne • GraphQL • PySpark • SQLAlchemy • PostgreSQL • Redis • React • TypeScript • AWS CDK • Terraform • EKS/ECS • Docker • GitHub Actions • Prometheus • Grafana • OpenTelemetry • Airflow • Spark Structured StreamingChallenging roles where I can blend deep Python craftsmanship, cloud architecture, and DevOps automation to deliver resilient products at scale—while mentoring teams on clean code and modern infra best practices.
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