AI Enablement Engineer

Revalize•Remote, UNITED STATES

13h•Remote

About The Position

The AI Enablement Engineer will help build, deploy, and support production AI tools, agents, and workflows used across Revalize. Working closely with business stakeholders and senior engineers, they help translate business needs into secure, reliable, and scalable solutions that integrate with enterprise platforms such as Salesforce and Microsoft 365. The role contributes across the full solution lifecycle - from scoping and development through launch, monitoring, user support, documentation, and continuous improvement - with guidance from more experienced team members. The ideal candidate is building strong software engineering and applied AI skills, brings a practical mindset, and communicates clearly with both technical and non-technical stakeholders.

Requirements

3+ years building software, with hands-on experience shipping LLM/generative AI features or systems beyond prototypes
Solid Python; working familiarity with REST APIs and at least one modern frontend framework (e.g. React with TypeScript)
Strong hands-on experience deploying and managing cloud solutions on Azure and/or AWS: services, functions or containers, scheduled jobs, monitoring, debugging from logs, and cost awareness
Hands-on experience with LLM tool-calling or agent frameworks, and with workflow/automation platforms (n8n, Power Automate, or similar)
Applied LLM fundamentals: RAG, embeddings, structured extraction, prompt design, and judgment about when outputs need human review
Experience integrating with at least one major enterprise platform (CRM, ERP, or Microsoft 365 APIs)

Nice To Haves

Experience with MCP servers, the Anthropic/Claude ecosystem, or enterprise AI platform administration
Azure or AWS certifications, or infrastructure-as-code experience (Terraform, Bicep)
Exposure to predictive ML (scoring, classification) or evaluation design for AI systems
Salesforce API and data-model experience; Microsoft Graph or Entra ID experience
Experience supporting or training end users - AI Champions work, past enablement experience, or teaching colleagues new tools
Shifts quickly in ambiguous situations and asks good questions rather than waiting for complete specifications
Communicates clearly with non-technical colleagues; writes documentation others can actually use

Responsibilities

Build and ship AI-powered tools and agents end to end: API integrations, agent workflows, backend services, and lightweight interfaces across our stack (Claude API, n8n, Copilot Studio, Azure AI Foundry, Power Automate)
Deploy and operate what you ship in Azure and AWS: manage services, scheduled jobs, capacity, and monitoring; investigate incidents; and support internal users - production means owned, not just launched
Integrate AI systems with Salesforce and Microsoft 365, designing within auth and permission models and security-first practices (least privilege, service accounts, audit logging)
Develop LLM pipelines that hold up in production: retrieval, structured extraction, scoring logic, prompt and tool design, and validation of outputs before they touch business systems
Use AI coding tools as a core working method, reviewing and governing their output to production quality - this team builds with AI, not just about it
Work directly with stakeholders across the business to scope requests, set expectations, and turn ambiguous asks into shippable tools
Document as you build - runbooks and decision notes that let others operate what you made