AI/ML Integration Engineer ( MCP/DMCP) - Build agents on Snowflake

AI/ML Integration Engineer ( MCP/DMCP)

Location: 100% Remote

Role Type: Contract‑to‑Hire

Work authorization: We are currently unable to provide visa sponsorship for this position

Focus: End‑to‑end Agent Development + Integrations + Snowflake‑centric AI Infrastructure

Client wants someone who can build agents on Snowflake

Role Summary

We are seeking a

hands-on AI/ML Integration Engineer

with strong experience in

DMCP and MCP protocols

,

Snowflake

, and

Claude

to build end‑to‑end AI agents and enterprise integrations. This role centers around designing semantic models for Slack, ingesting Salesforce customer chat data, and building agent workflows that rely on Snowflake as the core data and feature platform.

The engineer will also be responsible for building the required

infrastructure

, setting up

RBAC

, and developing secure, production-grade integrations that use Snowflake output data to power Claude-based agents.

Key Responsibilities

AI Agent Development (End‑to‑End)

• Build production-grade

  AI agents

  using Claude, Snowflake data outputs, and MCP/DMCP protocol integrations.

• Design agent workflows that consume Slack semantic models and Salesforce chat outputs.

• Implement retrieval, context assembly, and agent orchestration pipelines.

Protocol-Based Integrations (MCP / DMCP)

• Build and maintain

  MCP protocol integrations

  between Slack and Claude.

• Implement

  DMCP-based Snowflake → Claude

  integrations for agent data access.

• Ensure secure, reliable, and scalable protocol communication across systems.

Snowflake-Centric Data Engineering

• Ingest and model

  Salesforce customer chat data

  into Snowflake.

• Build semantic layers, feature tables, and agent-ready datasets.

• Develop ELT/ETL pipelines using Snowflake Streams, Tasks, Snowpipe, or dbt.

Slack Semantic Modeling

• Build

  semantic models for Slack

  conversations, channels, and message metadata.

• Structure Slack data for agent reasoning, retrieval, and workflow triggers.

Infrastructure & RBAC

• Stand up development and production environments for agent workloads.

• Implement

  RBAC

  , secrets management, and secure service-to-service communication.

• Build monitoring, logging, and observability for all integration services.