Role Summary
The Agentic AI Platform Engineer builds scalable reusable production-grade agentic infrastructure components. This role focuses on creating standardized Agentic AI constructs including MCP servers connectors agent orchestration templates memory frameworks evaluation pipelines and deployment patterns with enterprise-grade MLOps discipline.
Key Responsibilities
Agentic Platform Engineering (Build reusable Agentic components):
MCP (Model Context Protocol) servers MCP registries MCP gateways
Data Connectors (SQL Databricks NetDocs APIs)
Tool orchestration frameworks
Memory & context management services (Short term long term memory)
Create standardized agent templates for common agent patterns
LLM & Agent Orchestration (Design & deploy agents:
Multi-agent architectures and design patterns
Design Tool invocation frameworks memory management framework
Define and implement guardrails and policy enforcement at runtime.
MLOps & AI Engineering
Implement CI/CD for AI agents.
Manage model lifecycle using:
MLflow Model registry Version control for prompts and agents
Establish: Evaluation pipelines Observability Latency monitoring Hallucination detection Security testing
Role SummaryThe Agentic AI Platform Engineer builds scalable reusable production-grade agentic infrastructure components. This role focuses on creating standardized Agentic AI constructs including MCP servers connectors agent orchestration templates memory frameworks evaluation pipelines and deploy...
Role Summary
The Agentic AI Platform Engineer builds scalable reusable production-grade agentic infrastructure components. This role focuses on creating standardized Agentic AI constructs including MCP servers connectors agent orchestration templates memory frameworks evaluation pipelines and deployment patterns with enterprise-grade MLOps discipline.
Key Responsibilities
Agentic Platform Engineering (Build reusable Agentic components):
MCP (Model Context Protocol) servers MCP registries MCP gateways
Data Connectors (SQL Databricks NetDocs APIs)
Tool orchestration frameworks
Memory & context management services (Short term long term memory)
Create standardized agent templates for common agent patterns
LLM & Agent Orchestration (Design & deploy agents:
Multi-agent architectures and design patterns
Design Tool invocation frameworks memory management framework
Define and implement guardrails and policy enforcement at runtime.
MLOps & AI Engineering
Implement CI/CD for AI agents.
Manage model lifecycle using:
MLflow Model registry Version control for prompts and agents
Establish: Evaluation pipelines Observability Latency monitoring Hallucination detection Security testing
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