Job opportunity
Principal Platform Engineer
Insight Global
Insight Global is seeking a Principal Platform Engineer for a global retailer to take technical ownership of and scale an enterprise AI platform within GCP.
This is a senior individual contributor role with no direct reports, though this person will provide technical leadership and mentorship to engineering teams. The ideal candidate has personally built and operated enterprise AI/ML platforms in production, specifically within GCP and Vertex AI, and can own critical platform architecture decisions.
Responsibilities:
Own and evolve the enterprise AI platform architecture within GCP
Build and scale Vertex AI MLOps capabilities, including training pipelines, Model Registry, evaluation/approval workflows, and model serving
Establish MLOps/LLMOps standards across development, deployment, monitoring, and lifecycle management
Build GenAI platform capabilities including RAG, grounding, prompt management, guardrails, evaluations, agents, and MCP integration
Develop reusable Terraform modules, Infrastructure-as-Code, policy-as-code, and CI/CD pipelines
Create self-service “golden path” templates for engineering teams
Implement AI/ML observability and FinOps practices, including tracing, drift detection, latency, token usage, and cost monitoring
Establish platform standards around security, governance, scalability, and responsible AI
Remain hands-on with Python while owning key architecture decisions
Provide technical leadership and mentorship across engineering, data, architecture, security, and product teams
Required Skills & Experience
10+ years of experience across platform engineering, cloud infrastructure, software engineering, or enterprise architecture
Proven experience building and operating enterprise AI/ML platforms in production
Deep hands-on expertise with GCP and Vertex AI
Strong experience with MLOps and LLMOps architecture
Vertex AI experience across training pipelines, Model Registry, evaluation/approval, and model serving
GenAI experience with RAG, grounding, guardrails, evaluations, agents, and/or MCP
Strong Terraform, Infrastructure-as-Code, policy-as-code, and CI/CD experience
Experience building reusable/self-service platform capabilities for other engineering teams
Experience with AI/ML observability and FinOps
Advanced Python software engineering skills
Ability to independently own major platform architecture decisions
Pay Transparency:
Expected salary starting at $200,000
Vacancy Disclosure:
This role is for a new vacant opening within a growth project.
Please note, artificial intelligence tools are used to assist in screening and assessing applicants for this position.
#J-18808-Ljbffr
Apply on source site This is a senior individual contributor role with no direct reports, though this person will provide technical leadership and mentorship to engineering teams. The ideal candidate has personally built and operated enterprise AI/ML platforms in production, specifically within GCP and Vertex AI, and can own critical platform architecture decisions.
Responsibilities:
Own and evolve the enterprise AI platform architecture within GCP
Build and scale Vertex AI MLOps capabilities, including training pipelines, Model Registry, evaluation/approval workflows, and model serving
Establish MLOps/LLMOps standards across development, deployment, monitoring, and lifecycle management
Build GenAI platform capabilities including RAG, grounding, prompt management, guardrails, evaluations, agents, and MCP integration
Develop reusable Terraform modules, Infrastructure-as-Code, policy-as-code, and CI/CD pipelines
Create self-service “golden path” templates for engineering teams
Implement AI/ML observability and FinOps practices, including tracing, drift detection, latency, token usage, and cost monitoring
Establish platform standards around security, governance, scalability, and responsible AI
Remain hands-on with Python while owning key architecture decisions
Provide technical leadership and mentorship across engineering, data, architecture, security, and product teams
Required Skills & Experience
10+ years of experience across platform engineering, cloud infrastructure, software engineering, or enterprise architecture
Proven experience building and operating enterprise AI/ML platforms in production
Deep hands-on expertise with GCP and Vertex AI
Strong experience with MLOps and LLMOps architecture
Vertex AI experience across training pipelines, Model Registry, evaluation/approval, and model serving
GenAI experience with RAG, grounding, guardrails, evaluations, agents, and/or MCP
Strong Terraform, Infrastructure-as-Code, policy-as-code, and CI/CD experience
Experience building reusable/self-service platform capabilities for other engineering teams
Experience with AI/ML observability and FinOps
Advanced Python software engineering skills
Ability to independently own major platform architecture decisions
Pay Transparency:
Expected salary starting at $200,000
Vacancy Disclosure:
This role is for a new vacant opening within a growth project.
Please note, artificial intelligence tools are used to assist in screening and assessing applicants for this position.
#J-18808-Ljbffr