Senior Mlops/Ml Platform Engineer

TL;DR

Senior MLOps/ML Platform Engineer (AI Engineering): Designing, building, and operationalizing Machine Learning Proofs of Concept (POCs) that shape how ML is developed and scaled across the organization. Focus on implementing ML pipelines for training, inference, deployment, and monitoring, ensuring reliability, security, and performance of ML systems in production.

Location: Hybrid Work (home/office) in Istanbul, Turkey

Company

This is an exciting opportunity to join a newly launched ML initiative within a leading European fashion and retail company, where cutting-edge machine learning ideas are turned into real, production-ready solutions.

What you will do

• Design, build, and validate ML platform POCs across multiple use cases, working closely with Applied Scientists, ML Engineers, and Platform teams to deliver end-to-end ML workflows.
• Implement and operate ML pipelines for training, inference, deployment, and monitoring.
• Run and optimize ML workloads on Kubernetes, including GPU-based and multi-tenant environments.
• Evaluate and integrate ML platform tools and infrastructure into a shared, scalable platform.
• Support the early production rollout and integration into the existing ecosystem.
• Define best practices, governance, and onboarding standards for teams adopting the platform.

Requirements

• Strong experience building and operating production-grade ML platforms or large-scale data/ML systems on cloud infrastructure.
• Solid background in distributed systems, including containers (Docker), orchestration (Kubernetes), and streaming / batch processing (Kafka, Spark, Flink, etc.).
• Experience designing and operating scalable, low-latency, or high-throughput systems.
• Strong understanding of reliability, monitoring, and safe deployment practices (SLOs, incident response, capacity planning).
• Experience embedding security, IAM, and governance into platform workflows.
• Ability to evaluate, integrate, and operate multiple platform components into a coherent ML platform.

Nice to have

• Experience with Kubernetes-first ML systems, including running ML workloads on Kubernetes (EKS preferred) and multi-tenant and GPU-based environments.
• Experience with enterprise ML platforms (e.g. Databricks, Domino, ClearML) and feature platforms / feature stores (Feast, Hopsworks, etc.).
• Familiarity with governance and compliance in regulated ML environments.

Culture & Benefits

• Hybrid work (home/office).
• Paid vacation and holidays day off.
• Sport/insurance compensation, training compensation, and transportation compensation.
• English classes.
• Sick days.