What are the responsibilities and job description for the AI Platform Engineer/Architect- Need locals position at Jobs via Dice?
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This is a hands-on role for someone who can take messy real-world workflows and turn them into reliable internal systems.
You'll work closely with leadership, domain owners, and a knowledge engineer focused on workflow design, process mapping, and evaluation. The job is to understand how work actually happens, identify where AI creates leverage, and build the systems that make it dependable in practice.
You'll help build:
Orchestration and control-plane infrastructure for AI across functions
Connectors across tools, workflows, documents, and operational signals
Permissions, tracing, context services, and deployment plumbing
Sandboxed environments for testing AI systems against real operational data
Internal tools that bridge the gap between a prototype and something a team can rely on every day
This is not a generic AI engineer, prompt engineer, model-training, or applied-LLM feature role.
Requirements
What we're looking for:
Senior, staff, principal, or architect-level engineering experience
Strong systems depth across backend services, integrations, data flows, and application logic
Evidence of building internal platforms or workflow systems, not just end-user AI features
Practical daily use of LLMs as building tools
Clear judgment about deterministic versus model-driven system boundaries
Experience with human review, escalation, auditability, and operational safeguards
Willingness to work closely with teams to understand real workflows, not just stated requirements
You may be a strong fit if:
You've built internal platforms, shared services, or workflow systems used by multiple teams
You've designed connectors, integration patterns, tool contracts, or context layers
You've built AI systems for enterprise operations, security-sensitive workflows, compliance-heavy domains, or internal business processes
You are probably not a fit if:
Your background is mainly generic AI/ML feature work
Your strongest work is consumer search, ranking, recommendation, or shopping systems
Your work is mostly model training or research without substantial systems ownership
Your background is mostly prompt engineering, coursework, or hackathon projects
This is a hands-on role for someone who can take messy real-world workflows and turn them into reliable internal systems.
You'll work closely with leadership, domain owners, and a knowledge engineer focused on workflow design, process mapping, and evaluation. The job is to understand how work actually happens, identify where AI creates leverage, and build the systems that make it dependable in practice.
You'll help build:
Orchestration and control-plane infrastructure for AI across functions
Connectors across tools, workflows, documents, and operational signals
Permissions, tracing, context services, and deployment plumbing
Sandboxed environments for testing AI systems against real operational data
Internal tools that bridge the gap between a prototype and something a team can rely on every day
This is not a generic AI engineer, prompt engineer, model-training, or applied-LLM feature role.
Requirements
What we're looking for:
Senior, staff, principal, or architect-level engineering experience
Strong systems depth across backend services, integrations, data flows, and application logic
Evidence of building internal platforms or workflow systems, not just end-user AI features
Practical daily use of LLMs as building tools
Clear judgment about deterministic versus model-driven system boundaries
Experience with human review, escalation, auditability, and operational safeguards
Willingness to work closely with teams to understand real workflows, not just stated requirements
You may be a strong fit if:
You've built internal platforms, shared services, or workflow systems used by multiple teams
You've designed connectors, integration patterns, tool contracts, or context layers
You've built AI systems for enterprise operations, security-sensitive workflows, compliance-heavy domains, or internal business processes
You are probably not a fit if:
Your background is mainly generic AI/ML feature work
Your strongest work is consumer search, ranking, recommendation, or shopping systems
Your work is mostly model training or research without substantial systems ownership
Your background is mostly prompt engineering, coursework, or hackathon projects