Agentic AI Platform Engineer (with MLOps Expertise)

Prisma Sync Tech; Deutschland; Remote; EUR 90.000 - 140.000; Vollzeit

Zusammenfassung

Prisma Sync Tech is seeking an Agentic AI Platform Engineer to build scalable, production-grade agentic infrastructure components. You will standardize Agentic AI constructs including MCP servers, connectors, orchestration templates, and memory frameworks, applying enterprise-grade MLOps discipline.

You will design and deploy multi-agent architectures, tool invocation frameworks, and memory management solutions, while implementing guardrails and runtime policy enforcement.

Aufgaben

• Agentic Platform Engineering: Build reusable Agentic components
• MCP 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

Jobbeschreibung

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