MLOps Engineer

MLOps Engineer at Akvelon, Inc.

Akvelon, Inc. is building an internal AI platform designed to empower developers to efficiently ship AI-powered services. This role is central to improving Developer Experience (DevEx) and reducing time-to-market for AI features, focusing on model connectivity, prompt testing and evaluation, monitoring/observability, and core AI infrastructure.

As an MLOps Engineer, you will contribute to a long-term B2B contract, joining a growing team focused on cutting-edge AI platform development.

Tasks

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

Requirements

• Strong hands-on experience with Kubernetes in production environments, preferably AKS.
• Solid expertise in Terraform, including IaC best practices and multi-environment setups.
• Practical experience supporting ML/LLM workloads within a platform or DevOps/MLOps context.
• Proficiency in Python for automation, scripting, and supporting APIs/evaluation tooling.
• A clear understanding of CI/CD, release processes, and production-grade operations.
• Demonstrated ability to work under tight timelines and deliver pragmatic solutions.

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 various ML lifecycle tooling.

Key skills/competency

• MLOps
• Kubernetes
• Terraform
• AI Platform
• LLM Workflows
• CI/CD
• Observability
• Python
• Azure AKS
• Infrastructure as Code