Senior Platform ML Engineer - Freelance/B2B - Remote

Senior Platform ML Engineer - Freelance/B2B - Remote from the EU

Candidates MUST be based in the EU for this role.

We are unable to offer this opportunity to anyone outside the EU

6 months initially

Market rate

Must-Have Skills

Production ML platform experience

• 5+ years building and operating ML infrastructure or large-scale data/ML systems on cloud platforms
• Experience supporting mission-critical systems serving multiple teams

Distributed systems engineering

• Containers (Docker) and orchestration (Kubernetes)
• Experience with streaming and batch processing systems (eg, Kafka/Kinesis, Spark/Flink)

Low-latency and high-scale systems

• Experience designing and operating systems with strict latency and throughput requirements (eg, systems with sub-10ms inference or retrieval paths)
• Familiarity with caching, traffic shaping, and request management in production

Reliability, observability, and operability

• Designing systems with SLOs, monitoring, and safe deployment practices
• Experience with incident response, capacity planning, and post-incident reviews

Security and governance by design

• Experience working with IAM, secrets management, and network boundaries
• Ability to embed security, compliance, and governance into engineering workflows

Platform integration skills

• 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

Collaboration and communication

• 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

Nice-to-Have Skills

• Experience with enterprise ML platforms (eg, 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 optimizing GPU-based training workloads in shared, multi-tenant environments (eg, scheduling, utilization, 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
• MLOps awareness (cost attribution and optimization for ML workloads)
• Developer experience/platform enablement mindset (golden paths, templates, onboarding guides)

High-Level Tasks & Expectations

PoC Evaluations

• 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 scaling
• 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

Production Rollout

• Take responsibility for technical execution during early production rollout, working closely with internal teams who will own the platform long-term
• Contribute hands-on to building the production environment
• Support integration of the selected platform into the Client's ecosystem

Help define and implement:

• Initial best practices
• Governance and compliance foundations
• Operational ownership and maintenance boundaries

Support onboarding of:

• PoC use cases as first production adopters
• Additional early adopter teams
• Reduce friction and uncertainty for teams adopting new AI/ML use cases