Sr. MLOps Engineer Lenstra was created by the passion of engineers specialised in Computer Science with a proven history in delivering top quality solutions to its customers. Bringing together work excellence and vision we managed to serve top tier clients from a variety of industry domains like Banking/Insurance, Luxury and Tech.
We help our clients to solve their most difficult problems around Cloud Computing & DevOps, Data Platform, IT Security by having a holistic approach of their environment and building often complex but always relevant solutions to help them accelerate their business.
As a Senior MLOps Engineer, you will build and operate the platform and tooling that powers our client’s identity-verification products. You’ll join a team supporting Applied Scientists and Machine Learning Engineers across countries. For this mission you will help accelerate the path from ML research to production by building intuitive platform abstractions that let engineers focus on model innovation rather than infrastructure complexity.
Location : Based in France, Portugal, Spain or UK, fully remote under the condition to travel once in a while to one of the HQs.
Key Responsibilities:
Run and evolve the ML compute layer on Kubernetes/EKS (CPU/GPU) for multi-tenant workloads, and make workloads portable across regions (region-aware scheduling, cross-region data access, and artifact portability).
Operate Argo Workflows and Dask Gateway as reliable, self-serve services used by engineers and researchers to orchestrate data prep, training, evaluation, and large-scale batch compute (installation, upgrades, security, quotas, autoscaling).
Build GitOps-native delivery for ML jobs and platform components (GitLab CI, Helm, FluxCD) with fast rollouts and safe rollbacks.
Design and maintain the data platform built on LakeFS to enable experiment reproducibility, data lineage tracking, and automated governance processes.
Own developer experience and enablement by creating clear APIs/CLIs and minimal UIs, maintaining comprehensive templates and documentation.
Skills needed for the role:
Experience with distributed compute frameworks such as Dask, Spark, or Ray.
Familiarity with NVIDIA Triton or other inference servers.
FinOps best practices and cost attribution for multi-tenant ML infrastructure.
Exposure to multi-region designs (dataset replication strategies, compute placement, and latency optimization).
Container Orchestration: Kubernetes (EKS)
Compute: Argo Workflows for orchestration and Dask for Distributed Computing
ML Experiment Tracking: Weights & Biases
Data (Lakehouse & Versioning): Apache Iceberg + AWS Athena, LakeFS, Snowflake
CI/CD & GitOps: GitLab CI, Helm, FluxCD
Infrastructure as Code: Terraform
Observability: Prometheus/Grafana, Loki/Promtail, Datadog, Sentry
Languages & Libraries: Python (Django, FastAPI, Pydantic, boto3)