White Circle

4 weeks ago

MLOps Engineer

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 22 hours agoStatus: Live

Job Summary

MLOps Engineer at the boundary between Research and Production. You’ll own the infrastructure that makes trained models production-safe—building rollout pipelines, quality and latency gates, canary deployments, and dashboards to decide when to ship or rollback. You’ll operate GPU deployments on Kubernetes, implement CI/CD for model serving, and optimize cost and reliability across mixed GPU capacity.

Location: Paris, London

Workplace: Hybrid

Employment Type: Full time

Job Function: Software Engineering

Key Responsibilities

•Integrate new text and multimodal models into serving paths and verify they behave correctly under production-like traffic.
•Build and maintain rollout pipelines for frequent model releases.
•Create smoke, quality, and performance gates for model promotion.
•Operate local and cluster GPU deployments on Kubernetes.
•Build dashboards for latency, throughput, queue depth, GPU usage, fallback rate, and quality drift.

Pay and Benefits

Salary: USD 100,000 - 200,000 annually

Equity and Bonus:Equity

Perks:Health InsuranceRelocation Package

Key Requirements

•Experience with an inference serving engine (e.g., SGLang, vLLM, Dynamo, or TensorRT-LLM) and understanding of the request lifecycle through gateway, router, frontend, worker, queue, and model engine.
•Solid Kubernetes GPU experience (NVIDIA device plugin, GPU scheduling, resource requests/limits, node affinity, taints, tolerations, and node pools).
•Ability to design and implement CI/CD for model serving (image/config versioning, smoke tests, quality regression tests, latency/throughput gates, canary rollout, rollback).
•Strong observability to define dashboards/alerts for model promotion/rollback (latency at various percentiles, GPU utilization/memory, error/timeout/OOM rates, cost per successful request).
•Production debugging across the stack from Rust to k8s configs.

Experience:AIMLOpsLLMsMachine learningInference servingKubernetes

Skills:CommunicationProblem-solvingAttention to detail

Tech Stack:SGLangVLLMDynamoTensorRT-LLMKubernetesCUDARustPrometheusOpenTelemetryCI/CDGPUNVIDIA device pluginTerraformDCGMDatadogRoCEInfiniBand

Company Brief

White Circle

Builds AI-driven products and services to help businesses automate workflows, extract insights from data, and improve decision-making using machine learning and natural language processing technologies.

Industry: AI & Machine Learning

Website

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White Circle

4 weeks ago

MLOps Engineer

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 22 hours agoStatus: Live

Job Summary

Location: Paris, London

Workplace: Hybrid

Employment Type: Full time

Job Function: Software Engineering

Key Responsibilities

•Integrate new text and multimodal models into serving paths and verify they behave correctly under production-like traffic.
•Build and maintain rollout pipelines for frequent model releases.
•Create smoke, quality, and performance gates for model promotion.
•Operate local and cluster GPU deployments on Kubernetes.
•Build dashboards for latency, throughput, queue depth, GPU usage, fallback rate, and quality drift.

Pay and Benefits

Salary: USD 100,000 - 200,000 annually

Equity and Bonus:Equity

Perks:Health InsuranceRelocation Package

Key Requirements

•Experience with an inference serving engine (e.g., SGLang, vLLM, Dynamo, or TensorRT-LLM) and understanding of the request lifecycle through gateway, router, frontend, worker, queue, and model engine.
•Solid Kubernetes GPU experience (NVIDIA device plugin, GPU scheduling, resource requests/limits, node affinity, taints, tolerations, and node pools).
•Ability to design and implement CI/CD for model serving (image/config versioning, smoke tests, quality regression tests, latency/throughput gates, canary rollout, rollback).
•Strong observability to define dashboards/alerts for model promotion/rollback (latency at various percentiles, GPU utilization/memory, error/timeout/OOM rates, cost per successful request).
•Production debugging across the stack from Rust to k8s configs.

Experience:AIMLOpsLLMsMachine learningInference servingKubernetes

Skills:CommunicationProblem-solvingAttention to detail

Tech Stack:SGLangVLLMDynamoTensorRT-LLMKubernetesCUDARustPrometheusOpenTelemetryCI/CDGPUNVIDIA device pluginTerraformDCGMDatadogRoCEInfiniBand

Company Brief

White Circle

Industry: AI & Machine Learning

Website