Overview
Contract - W2
Contract - 2 day((s))
Skills
Kubernetes
Dataflow
Infrastructure as Code (Terraform preferred
also CloudFormation or similar tools) Cloud-native CI/CD tools: Jenkins
GitLab
Bamboo
TeamCity
CodeDeploy (AWS) Microservices and distributed architectures: Containers
Serverless GCP Native Tools: BigQuery
Cloud Storage
GKE
Cloud Run
PubSub
VertexAI MLOps principles and tools; prompt engineering; context management; embedding techniques
Job Details
Job Summary (DevOps Engineer with Agentic AI Stack knowledge):
- Design, implement, and maintain Infrastructure as Code solutions (preferably using Terraform, CloudFormation, or similar tools).
- Develop and manage cloud-native CI/CD pipelines utilizing tools such as Jenkins, GitLab, Bamboo, TeamCity, and AWS CodeDeploy.
- Support and deploy microservices and distributed architectures using containers, Kubernetes, and serverless technologies.
- Work extensively with Google Cloud Platform (Google Cloud Platform) native tools, including BigQuery, Cloud Storage, Dataflow, GKE, Cloud Run, PubSub, and VertexAI.
- Apply MLOps principles, prompt engineering, context management, and embedding techniques to AI/ML workflows.
- (Preferred) Participate in AI/ML lifecycle management and work with frameworks such as TensorFlow, PyTorch, and BigQuery ML.
- (Preferred) Contribute to projects involving Generative AI and Large Language Models (LLMs) on Google Cloud Platform.
- (Preferred) Utilize Agentic AI frameworks/libraries (e.g., Google ADK, Model Context Protocol, LangGraph) for advanced AI development.
- (Preferred) Engage in full-stack development within cloud environments, leveraging Python and relevant libraries for Agentic AI framework development.
- Collaborate with cross-functional teams to deliver robust, scalable, and efficient DevOps solutions for AI-driven products and services.
- Design, implement, and maintain Infrastructure as Code solutions (preferably using Terraform, CloudFormation, or similar tools).
- Develop and manage cloud-native CI/CD pipelines utilizing tools such as Jenkins, GitLab, Bamboo, TeamCity, and AWS CodeDeploy.
- Support and deploy microservices and distributed architectures using containers, Kubernetes, and serverless technologies.
- Work extensively with Google Cloud Platform (Google Cloud Platform) native tools, including BigQuery, Cloud Storage, Dataflow, GKE, Cloud Run, PubSub, and VertexAI.
- Apply MLOps principles, prompt engineering, context management, and embedding techniques to AI/ML workflows.
- (Preferred) Participate in AI/ML lifecycle management and work with frameworks such as TensorFlow, PyTorch, and BigQuery ML.
- (Preferred) Contribute to projects involving Generative AI and Large Language Models (LLMs) on Google Cloud Platform.
- (Preferred) Utilize Agentic AI frameworks/libraries (e.g., Google ADK, Model Context Protocol, LangGraph) for advanced AI development.
- (Preferred) Engage in full-stack development within cloud environments, leveraging Python and relevant libraries for Agentic AI framework development.
- Collaborate with cross-functional teams to deliver robust, scalable, and efficient DevOps solutions for AI-driven products and services.
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.