Senior MLOps/ML Platform Engineer (AI/ML)

TL;DR

Senior MLOps/ML Platform Engineer (AI/ML): Building and validating Machine Learning Proofs of Concept (POCs) that shape how ML is developed and scaled across the organization with an accent on platform-level evaluation, implementation, and early production rollout. Focus on designing, building, and operationalizing reusable ML solutions, optimizing ML workloads on Kubernetes, and defining best practices for platform adoption.

Location: Wroclaw, Poland

Company

hirify.global is a leading European fashion and retail company focused on turning cutting-edge machine learning ideas into production-ready solutions.

What you will do

• Design, build, and validate ML platform POCs across multiple use cases.
• Deliver end-to-end ML workflows by implementing and operating ML pipelines for training, inference, deployment, and monitoring.
• Run and optimize ML workloads on Kubernetes, including GPU-based and multi-tenant environments.
• Evaluate, integrate, and support the early production rollout of ML platform tools and infrastructure into the existing ecosystem.
• Define best practices, governance, and onboarding standards for teams adopting the platform.
• Ensure reliability, security, and performance of ML systems in production.

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).
• Experience designing, operating, and ensuring reliability, monitoring, and safe deployment of scalable, low-latency, or high-throughput systems.
• Experience embedding security, IAM, and governance into platform workflows.
• Ability to evaluate, integrate, and operate multiple platform components into a coherent ML platform.
• Strong communication skills to produce architecture designs, POC findings, and technical recommendations.

Nice to have

• Experience with Kubernetes-first ML systems, including running ML workloads on Kubernetes (EKS preferred), multi-tenant, and GPU-based environments.
• Familiarity with enterprise ML platforms (e.g., Databricks, Domino, ClearML) and feature platforms (Feast, Hopsworks).
• Experience with governance and compliance in regulated ML environments.
• FinOps awareness for ML infrastructure costs.
• Focus on developer experience and platform enablement (templates, golden paths, onboarding flows).

Culture & Benefits

• Paid Vacation and Sick Days.
• Sport/Insurance Compensation.
• Company-sponsored English Classes.
• Charity initiatives.
• Training Compensation for professional development.