Source description
About the role
To be able to perform this responsibilities as expected by the client, the candidate must fulfil two important requirements that are broad areas of knowledge and hands-on implementation experience:
DevOps experience with a focus on Kubernetes
Agentic AI experience, with a focus on lifecycle platforms (e.g. LangFuse)
Regarding DevOps skills, the candidate is expected to have at least 4 years of job experience in roles such as DevOps and/or backend engineer . Additionally, the candidate is expected to possess areas of knowledge that are broadly related to the topics listed below. It is not required that the candidate has experience or knowledge on all of the listed topics, or on any one in particular , the list is intended to be just a guideline of the kinds of things we are looking for:
General knowledge of Linux OS administration
General knowledge of containers, images and container registries
Concepts of IAM and RBAC
Internal Development Platforms (IDP), self-service infrastructure, golden paths
General knowledge of IaC concepts and hands on experience with Terraform
General knowledge Kubernetes, its entities and networking with hands on experience
Kubernetes administration, helm charts and automations
Standardizations of development processes
Templates and scaffolding for development on Kubernetes
Design, implementation and operation of CI/CD pipelines and related frameworks
GitHub actions or similar frameworks
CI/CD frameworks on Kubernetes
Monitoring and observability on Kubernetes
ML frameworks for Kubernetes (Kserve, Kubeflow, Ray, etc)
Regarding Agentic AI skills, the candidate is expected to have at least 1 to 2 years of job experience in related roles . Additionally, the candidate is expected to possess areas of knowledge that are broadly related to the topics listed below. It is not required that the candidate has experience or knowledge on all of the listed topics, or on any one in particular , the list is intended to be just a guideline of the kinds of things we are looking for:
General knowledge of commercial and open source LLMs and their trade offs
Prompt engineering
Prompt templating, versioning, A/B testing and management
Prompt guardrails design and implementation, prompt injection defense
Agentic skills specification and best practices
Agentic orchestration frameworks (LangChain, LangGraph, etc)
Autonomous agent architectures
Frameworks for monitoring, tracing,debugging and evaluating agentic flows (LangSmith, LangFuse, etc)
General knowledge of text embedding models
Vector databases, semantic search, RAG
Cost optimization of prompts and agentic pipelines
External memory layer and tools
MCP frameworks
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