Senior ML Platform Engineer – ML Platforms & MLOps

Project – the aim you’ll have
Our client is a leading e-commerce company specialized in fashion, shoes, accessories, beauty – i.e. retail / online fashion platform.
We are looking for an experienced Senior ML Platform Engineer to design, build, and scale machine learning platforms and MLOps tooling. You will work at the intersection of software engineering and machine learning, enabling teams to develop, deploy, and operate ML models reliably in production. You will join a team that values high engineering standards, automation, fast delivery of business value, and close collaboration with data science, product, and infrastructure teams.

• Support and contribute hands-on to multiple ML platform POCs
• Work closely with Applied Scientists, ML Engineers, and internal platform teams
• Evaluate platform capabilities across:
• GPU training and experimentation
• Real-time and batch inference
• Orchestration, monitoring, and operability
• Multi-tenancy, isolation, and scalability
• Assess integration points with existing in-house tooling
• Perform performance and operability analysis
• Contribute technical input to:
• Build vs buy vs extend decisions
• Target platform stack recommendations
• OPEX and CAPEX justification for rollout

• 5+ years building and operating ML infrastructure or large-scale data/ML systems on cloud platforms
• Experience supporting mission-critical systems serving multiple teams
• Containers (Docker) and orchestration (Kubernetes)
• Experience with streaming and batch processing systems (e.g. Kafka/Kinesis, Spark/Flink)
• Experience designing and operating systems with strict latency and throughput requirements (e.g. systems with sub-10ms inference or retrieval paths)
• Familiarity with caching, traffic shaping, and request management in production
• Designing systems with SLOs, monitoring, and safe deployment practices
• Experience with incident response, capacity planning, and post-incident reviews
• Experience working with IAM, secrets management, and network boundaries
• Ability to embed security, compliance, and governance into engineering workflows
• Experience combining multiple platform components (open source and managed services) into a coherent, shared, multi-team, production-ready ML platform
• Comfortable evaluating and integrating tools rather than relying on a single end-to-end solution
• Evaluating build vs buy vs extend trade-offs
• Clear articulation of technical trade-offs and recommendations
• Ability to produce architecture designs, POC findings, and decision input
• Effective collaboration with platform, infra, and ML teams

• Experience with enterprise ML platforms (e.g. Databricks, Domino, ClearML)
• Kubernetes-first ML systems
• Hands-on experience running ML workloads on Kubernetes (EKS preferred)
• Multi-tenant environments, resource isolation, autoscaling
• Experience running and optimising GPU-based training workloads in shared, multi-tenant environments (e.g. scheduling, utilisation, cost efficiency).
• Feature platform or feature store experience
• Online/offline consistency, schema evolution
• Familiarity with Hopsworks, Feast, or similar
• Governance and compliance experience in regulated ML environments
• Experience onboarding teams onto shared platforms
• FinOps awareness (cost attribution and optimisation for ML workloads)
• Developer experience / platform enablement mindset (golden paths, templates, onboarding flows)