Akvelon, Inc.

MLOps Engineer

Akvelon, Inc.

contract

Posted on:

Location Type: Remote

Location: Serbia

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Tech Stack

AzureKubernetesPythonTerraform

About the role

  • Build and operate the AI platform infrastructure enabling developers to ship LLM-based services faster.
  • Implement and maintain Kubernetes-based runtime environments (incl. AKS) for AI workloads.
  • Manage infrastructure as code with Terraform (modules, environments, CI/CD automation).
  • Support LLM workflows: RAG, agents, prompt experimentation, evaluations, and deployment patterns.
  • Integrate and operate tooling such as Azure AI Foundry, LiteLLM, Langfuse, MLflow.
  • Orchestrate pipelines using Kubeflow Pipelines and/or Argo Workflows (build, deploy, evaluate).
  • Improve platform reliability and observability (monitoring, logging, tracing, cost/perf signals).
  • Collaborate closely with developers to streamline DX (APIs, templates, docs, golden paths, automation).

Requirements

  • Strong hands-on experience with Kubernetes in production (preferably AKS).
  • Solid Terraform expertise (IaC best practices, multi-env setups).
  • Practical experience supporting ML/LLM workloads in a platform or DevOps/MLOps context.
  • Proficiency in Python for automation, scripting, and supporting APIs/evaluation tooling.
  • Understanding of CI/CD, release processes, and production-grade operations.
  • Ability to work under tight timelines and deliver pragmatically.
  • Nice to Have: Experience building internal developer platforms or “paved roads” for engineering teams.
  • Familiarity with LLM evaluation frameworks, prompt testing workflows, and LLM observability.
  • Exposure to RAG architectures, vector databases, and agentic patterns.
  • Experience with Kubeflow, Argo, and ML lifecycle tooling.
Benefits
  • None specified 📊 Check your resume score for this job Improve your chances of getting an interview by checking your resume score before you apply.
Applicant Tracking System Keywords

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Hard Skills & Tools
KubernetesTerraformPythonCI/CDML/LLM workloadsKubeflowArgo WorkflowsInfrastructure as CodeMonitoringLogging
Soft Skills
collaborationtime managementpragmatism