Senior AI Solutions Engineer

Confidential

Remote job permanent

Posted: January 30, 2026

Job Description

We are looking for a Senior AI Solutions Engineer to join our Product & Innovation team as we build Elevate, our proprietary Marketing Intelligence platform centered around a “Universal AI Agent.” Our mission is to unify internal tools and client-facing agents into a single, autonomous ecosystem powered by the Model Context Protocol (MCP).
In this role, you will act as a Senior Individual Contributor with full architectural autonomy - designing, building, and deploying advanced AI and automation solutions that bridge low-code orchestration with custom backend engineering to solve high-value business and client challenges.

Responsibilities

Product Engineering (Elevate & Core Infrastructure)

• Architect the Universal Agent: Design the central orchestration layer for our "Elevate" platform, allowing the AI to autonomously perform complex actions across integrated marketing tools.

• Implement MCP Strategy: Lead the adoption of the Model Context Protocol (MCP) to standardize how our agents connect to external APIs, databases, and third-party platforms.

• State & Memory Management: Architect the system’s persistent memory using Vector Databases (RAG) to ensure agents maintain context across long-term interactions and distinct sessions.

Client & Agency Automation

• Bespoke Agent Development: Prototype and deploy specialized AI agents for our B2B clients (e.g., Lead Qualification Bots, Data Analysis Pipelines, Automated Reporting).

• Operational Efficiency: Analyze internal agency processes and replace manual workflows with robust, error-tolerant automation.

• Stack Selection: Assess specific project requirements to determine the optimal approach—choosing between low-code orchestration (n8n) or custom code (Python/TypeScript) based on scalability and complexity needs.

Technical Implementation

• Advanced Integrations: Build custom connectors for undocumented or complex APIs, handling authentication (OAuth2, API Keys) and data transformation programmatically.

• Custom Tooling: Extend the capabilities of standard automation platforms by writing custom nodes, scripts, and microservices in TypeScript or Python.

• Reliability Engineering: Ensure all workflows are production-grade, implementing comprehensive error handling, logging, and rate-limit management strategies.

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