AI role · Open offer

Lead MLOps Engineer — GPU clusters & vLLM

Market index

Focus: Enterprise AI

Paris · 1000-2000 employees · Scale-up · GenAI: Partial

Freelance
Full-time
Lead
ML platform
Hybrid
Amsterdam, Netherlands · Hybrid
€95k–€125k or competitive day-rate path

This listing is optimized for AI-native talent: clear skills and tools so candidates know if they fit before they apply—and so you spend less time screening mismatches.

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At a glance

How you'll work, where, and key listing dates.

Employment: Freelance, Full-time
Workplace: Hybrid
Location: Amsterdam, Netherlands · Hybrid
Compensation: €95k–€125k or competitive day-rate path
Experience level: Lead
Team: ML platform
Benefits & perks

About this role

What the team is looking for and how this position fits.

Dataiku is hiring a Lead Lead MLOps Engineer — GPU clusters & vLLM on the ML platform team (Hybrid · Amsterdam, Netherlands). This is a hands-on role in ML platform & MLOps—not a generic “AI enthusiast” post.

You will own training, deployment, observability, and cost control end to end: problem framing, offline evaluation, safe rollout, and iteration from production signals. Success is measured with clear metrics (quality, latency, cost, or business KPIs)—not slide decks alone.

Our Amsterdam-based ML platform team owns GPU pools, model registry, and predictable rollouts for dozens of internal teams. You will set technical direction as we expand multi-cloud options.

Hybrid role with flexible EU remote weeks; on-call rotation is shared and compensated.

What you will do

Own vLLM/ONNX gateway roadmaps, scaling tests, and failover drills.
Define FinOps guardrails: quotas, chargeback views, and alerting for runaway jobs.
Partner with security for image hardening, artefact signing, and egress policies.
Mentor engineers on distributed systems debugging and post-incident learnings.
Evaluate vendor accelerators (Run:ai, Determined, SageMaker) with pragmatic scorecards.

What we look for

7+ years infra/platform experience with hands-on Kubernetes and cloud networking.
Track record improving GPU utilisation and inference cost metrics measurably.
Comfort translating DS needs into SLO-backed platform features.

Nice to have

NVIDIA ecosystem fluency (CUDA, Triton, NCCL tuning).
OpenTelemetry champion.

Skills we're hiring for

What each capability means for this role—not a keyword checklist.

Python
Production Python for data pipelines, model code, APIs, and automated tests—not notebook-only workflows.
Kubernetes
Deploying and scaling model services with health checks, autoscaling, and rollout discipline.
AWS

Tools & stack

Platforms and products mentioned for this role (APIs, IDEs, vector DBs, CRMs, design tools, etc.).

PostgreSQLTool
Regular use of PostgreSQL in the ML platform & MLOps stack—configuration, debugging, and team conventions.

You're a strong fit if…

Tailored to this listing—use it as a self-check before you apply.

You can show evidence in your application: Describe one incident where you rolled back a bad model version safely.
Several of these show up in recent shipped work—not only on your CV: Python, Kubernetes, AWS, Docker, and PostgreSQL.
You have led ML platform & MLOps initiatives across teams—setting technical direction, review standards, and rollout plans others could follow.
You are comfortable with the hybrid rhythm (Hybrid · Amsterdam, Netherlands)—onsite collaboration when it matters, deep work when remote.
You explain trade-offs (quality, cost, latency, safety) in plain language—with numbers or examples, not buzzwords.
You read the full listing (comp: €95k–€125k or competitive day-rate path, benefits in At a glance) and your expectations align.

Apply

Candidate accounts only: add your headline and bio on , and optionally a CV link or PDF so recruiters see your full résumé. Then write a short pitch below—applications use your login email.

What the employer asked for

Describe one incident where you rolled back a bad model version safely.

Register, add a headline and bio, then you can attach a CV link or PDF on My account before you apply. That keeps applications tied to your profile and makes it easier for employers to review your background.

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