Model registry + versioning
MLflow, Weights & Biases, Vertex Model Registry, SageMaker Model Registry. Promotion gates, audit trails, lineage from training run to production.
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Hire MLOps engineers · Amsterdam
MLOps as production engineering: model registries, eval harnesses, drift monitoring, A/B testing, and the operational substrate that distinguishes shipped ML from notebook demos.
Hiring MLOps engineers in Amsterdam
Amsterdam, Netherlands engagements ship with the same seniorMLOps bench used across every Prosigns delivery. No staff-augmentation, no junior fallback, no SDR routing. The engineers in the proposal are the engineers in production.
Why senior, not contractor
Most ML in production today doesn’t have a registry, doesn’t have evals in CI, doesn’t monitor drift, and ships new models the same way it shipped the first one: by re-running a notebook. The MLOps gap is where ML engagements quietly fail six months after launch. Prosigns ships MLOps as production substrate: registries with audit trails, eval harnesses gating every model change in CI, drift monitoring with alerting, A/B testing infrastructure, and rollback semantics that survive a vendor model changing its behavior overnight.
Senior floor
G6+ minimum
Bench depth
15+ G6/G9 engineers
In production
2019+
Engagement
Outcome-led SOW
Where MLOps ships
Specific applications of MLOpswe’ve built and operate. Every example below maps to a real engagement, not a bullet on a stack-card.
MLflow, Weights & Biases, Vertex Model Registry, SageMaker Model Registry. Promotion gates, audit trails, lineage from training run to production.
Per-use-case eval suites: RAG faithfulness, agent task success, classification calibration. Gating model + prompt changes before deploy.
Evidently, WhyLabs, Arize, Fiddler. Distribution drift, prediction drift, performance drift. Alerting wired into incident response.
Ray Serve, Triton, BentoML, vLLM, KServe. Autoscaling, fallback behaviors, cost-aware routing across models / providers.
Feast, Tecton, Hopsworks. Online + offline features with consistency, lineage, and time-travel for training reproducibility.
Statsig, GrowthBook, LaunchDarkly + custom. Model A/B tests, prompt A/B tests, multi-armed bandits where they fit.
Stack depth
Frameworks, libraries, and runtime tools the bench has shipped in production. Not a CV-skim, a working depth.
Registries + tracking
Inference
Monitoring
Feature stores
Orchestration + experimentation
Engagement models
We don’t bill hourly contractors. Engagements run against outcomes, choose the shape that matches the work.
See engagement modelsFixed-scope
When the deliverable is clear and the scope is bounded: an MVP, a migration, a discrete platform build. Senior engineering against a written outcome, not against a body count.
Embedded squad
When the work is product-shaped and the cadence is continuous. A senior pod (engineering + design + PM as needed) embedded into your team, with the practice lead co-piloting from HELM.
Managed services
When the system is running and needs ongoing engineering ownership: operations, SLO defense, release management, security and compliance evidence. Monthly retainer against a published SLA.
Selected work
Financial services
MLflow registry with promotion gates, Evidently drift monitoring with PagerDuty alerting, BentoML inference with autoscaling, eval harnesses in GitHub Actions. Cleared the first model-risk-management audit on the new substrate.
Duration · 4 months
Brief us
Reply < 4 business hoursFive fields. Goes straight to the practice lead, not an SDR. We’ll reply with a senior engineer’s read on fit, scope, and the engagement model that suits the work.
FAQ
Everything below also appears in the proposal and the SOW: no surprises after signing.
Not always. For batch-predict use cases or single-team ML, a feature store often adds complexity that doesn't pay for itself. For multi-team ML with online inference and shared features across models, a feature store is genuinely the right tool. We’ll tell you which fits. We don’t recommend feature stores by default.
LangFuse / LangSmith / Phoenix for trace collection across the full agent or RAG pipeline. Per-use-case quality dashboards (faithfulness, latency, token cost). Eval harnesses in CI gating prompt + model changes. The same operational discipline as classical ML. Adapted for the LLM-specific failure modes.
Engineering-led delivery. We don't bill hourly contractors against your JIRA board. Every engagement runs against a defined outcome with a senior engineer accountable from kickoff to operating cutover. If you genuinely need staff-aug, discrete bodies, your management, hourly rates, we'll be honest and route you to a partner that fits.
G6 minimum (six-plus years in their craft) on every billable hour. Department leads are G9 or G10. We don't flex juniors onto the bench mid-sprint, we don't subcontract to delivery centers, and we don't dilute senior rates with mixed staffing. The bench in the proposal is the bench in production.
Three engagement models published at /engagement-models/. Fixed-scope for defined deliverables, embedded squads for ongoing product work, managed services for steady-state operations. Rates depend on seniority, engagement length, and region. Discovery + scoping conversation is free; SOWs are written against deliverables, not bodies.
Senior-only across Dallas, Doha, Lahore, and Islamabad. We staff against the engagement's needs (timezone, language, regulatory frame), not against arbitrary regional preferences. Most engagements run with a US/EU-aligned core and a follow-the-sun extended bench when the workload warrants it.
Yes. We name the engineers in the SOW, attach their profiles, and they're on the kickoff. We don't bait-and-switch with senior reviewers and junior execution. If a named engineer needs to roll off the engagement (rare), we surface a replacement from the same seniority tier with explicit handoff.
Talk to a MLOps lead
Bring the workload: we’ll bring a senior engineer plus the practice lead most relevant to the work. 30 minutes, no obligation, no junior reps.