AI/ML Integration Engineer ( MCP/DMCP)

Hello Everyone,

Hope you are doing good!!!!

My name is Pavan and I work with SPAR Information System., I have a great opportunity for you, please find the job details below, if you are interested in applying please send me your updated resume and best time for you to discuss about this opportunity in details.

Title: AI/ML Integration Engineer ( MCP/DMCP)
Location: Remote
Duration: 3 -6 Months Contract to hire or long term contract

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

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.

Thanks & Regards,

Pavan Raikhelkar

LEAD TALENT ACQUISITION SPECIALIST

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