/ TEAMS · Specialist engineering · MLOps / inference
Source engineers experienced in Model serving, deployment pipelines and operational ML. for business-critical production systems, safe integrations and measurable technical ownership.
2,000+ engineers in our active talent network · Regional recruiters
/ SYSTEM STACK
model serving pipelines
model registry versioning
feature data contracts
drift monitoring
inference latency
/ AVAILABLE TO HIRE
Profiles show the experience we can source. Individual candidate availability is confirmed after enquiry.
Senior MLOps / Inference Engineer
Available14+ years of experience · Romania
MLOps / Inference Engineer with 14 years in production systems, focused on model serving pipelines and model registry versioning.
Languages: Romanian, English B2+
Mid-level MLOps / Inference Engineer
Inquire availability3+ years of experience · Czechia
MLOps / Inference Engineer with 3 years in production systems, focused on model registry versioning and feature data contracts.
Languages: Czech, English C1
Mid+ MLOps / Inference Engineer
Inquire availability7+ years of experience · Lithuania
MLOps / Inference Engineer with 7 years in production systems, focused on feature data contracts and drift monitoring.
Languages: Lithuanian, English B2+
Senior MLOps / Inference Engineer
Available7+ years of experience · Poland
MLOps / Inference Engineer with 7 years in production systems, focused on drift monitoring and inference latency.
Languages: Polish, English B2+
/ Engineering teams
Onectus uses its active engineering network and regional recruiting partners to search against concrete systems, environments and responsibilities. We evaluate practical experience rather than treating a framework mention or certification as proof of delivery. Particular availability and engagement terms are confirmed after an enquiry. For this role, practical knowledge of model serving pipelines, model registry versioning, feature data contracts helps define the engineering search.
/ Technical specializations
The right profile depends on your actual architecture, performance constraints, integration boundaries and operational responsibilities.
MLOps / inference responsibilities include model serving pipelines, related model registry versioning, clear test coverage and a reliable production handover. Examine the actual tooling and deployment environment.
MLOps / inference responsibilities include model registry versioning, related feature data contracts, clear test coverage and a reliable production handover. Examine the actual tooling and deployment environment.
MLOps / inference responsibilities include feature data contracts, related drift monitoring, clear test coverage and a reliable production handover. Examine the actual tooling and deployment environment.
MLOps / inference responsibilities include drift monitoring, related inference latency, clear test coverage and a reliable production handover. Examine the actual tooling and deployment environment.
MLOps / inference responsibilities include inference latency, related rollout rollback strategies, clear test coverage and a reliable production handover. Examine the actual tooling and deployment environment.
MLOps / inference responsibilities include rollout rollback strategies, related model serving pipelines, clear test coverage and a reliable production handover. Examine the actual tooling and deployment environment.
/ Project scenarios
Operationalize a tested ML model. Define constraints around model serving pipelines, map dependencies and test how the proposed implementation behaves under real operating conditions.
Stabilize inference latency spikes. Define constraints around model registry versioning, map dependencies and test how the proposed implementation behaves under real operating conditions.
Detect model data drift. Define constraints around feature data contracts, map dependencies and test how the proposed implementation behaves under real operating conditions.
Roll back unsafe model releases. Define constraints around drift monitoring, map dependencies and test how the proposed implementation behaves under real operating conditions.
/ Practical evaluation
A senior engineering interview should surface directly relevant implementation details, debugging history and operational judgment.
Describe a production issue involving model serving pipelines and what evidence led to a fix.
Which decisions around model registry versioning and feature data contracts were you personally accountable for?
Describe a production issue involving feature data contracts and what evidence led to a fix.
Which decisions around drift monitoring and inference latency were you personally accountable for?
/ Sourcing process
Define platform versions, deployment constraints, traffic patterns, required integrations and ownership.
Search the active network and partner channels for platform-specific engineering experience.
Discuss real systems, code or configuration decisions, troubleshooting and change control.
Confirm role boundaries, location or remote access, availability, security and interviews.
Profiles, availability and engagement terms are confirmed for each request.
/ Questions
Yes. Tell us the relevant versions, operational environment and the responsibilities connected to model serving pipelines. We qualify hands-on experience during each search.
These can be included when central to the project. We distinguish practical implementation work from general familiarity and ask about failure scenarios and testing.
We look at production decisions, trade-offs, incident diagnosis, code or infrastructure ownership and the ability to explain decisions without relying on a technology checklist.
Share your architecture, technical constraints, collaboration model and priorities. We can then discuss relevant sourcing channels, evaluation criteria and next steps.
/ Onectus · TEAMS
Tell us the technology, production responsibilities, experience level and working model. We'll activate the appropriate engineering sourcing channels.
