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Senior MLOps Engineer (Vietnam)

Tensormesh· Core Engineering
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Hybrid (Vietnam)Full-timePosted 3mo ago

About the role

Senior MLOps Engineer role focused on building and maintaining the MLOps infrastructure for Tensormesh.ai, with emphasis on GitHub Actions, GPU CI/CD, release engineering, performance benchmarking, security, and contributor experience. Remote opportunity from Vietnam.

Responsibilities

  • Pipeline architecture: GitHub Actions workflows + self-hosted GPU runner fleet (H100/A100); multi-stage pipeline from lint → unit → GPU integration → cross-framework compat (vLLM/SGLang) → performance regression
  • Release engineering: semantic versioning, PyPI publishing, multi-arch container images, Helm charts, Sigstore/cosign signing, coordination with downstream integrators
  • Performance gates: Continuous benchmarking that blocks regressions in cache hit rate, TTFT, throughput, memory before merge
  • Contributor experience: fast PR feedback, eliminate flakiness, dev containers that don't require expensive GPUs
  • Security & IaC: SBOM/SLSA provenance, secret rotation, runner fleet via Terraform with cost-optimized autoscaling.

Qualifications

  • 4+ years MLOps/DevOps/SRE; 2+ years CI/CD for GPU or ML workloads
  • Deep GitHub Actions expertise (workflows, composite actions, self-hosted runners at scale)
  • Python packaging & PyPI release flow (incl. wheels with native extensions)
  • Docker multi-stage/multi-arch; NVIDIA Container Toolkit
  • Terraform/Ansible for cloud GPU infrastructure
  • Track record building CI that contributors trust — fast, non-flaky, clear failures

Skills

Required:MLOps / DevOps / SREGitHub ActionsPython packagingDocker / containerizationTerraform / Ansible

Benefits

  • Competitive global compensation
  • Engineering-first culture with focus on code, impact and learning
  • Remote/hybrid flexibility
Last updated Jul 5, 2026. Interested? Apply on the company site.

About Tensormesh

Cached tokens

Tensormesh caches repeated prompts, documents, tools, and workflow context, helping your AI apps run faster and cheaper with $0 cached tokens.

AI InferenceCachingCloud/InfrastuctureContext CachingCost ReductionOpenAI-Compatible APIReservedServerless
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