Company
Technology
AIEnablementEngineer
Neural analysis suggests this role is
optimal for Senior candidates.
“AI Enablement Engineer. Skills: AI platform, Internal tools, Automation workflows. Build and evolve an internal AI platform. Partner with cross-functional teams to identify needs”
What You'll Achieve.
Enhance productivity across all teams; Deliver features and improvements
Industry & Context.
Root cause analysis
What They're Looking For.
Must Have
5+ years of experience in software engineering, platform engineering, or AI/automation-focused development, Hands-on experience with AI systems, particularly Anthropic Claude or similar LLM-based tools, Proficiency in Python, Experience working with REST APIs, webhooks, JSON, and system integrations, Demonstrated ability to design reusable systems and scalable architectures, Experience building internal tools, automation workflows, or platform-level engineering solutions, Understanding of secure software development practices, secrets management, and data protection principles, Awareness of AI security risks such as prompt injection, data leakage, and misuse of LLM outputs, Excellent communication skills, Ability to translate non-technical workflows into AI-driven solutions, Ability to operate independently, prioritize effectively, and manage a roadmap without heavy supervision
Nice to Have
Experience working with internal stakeholders across business functions, Running enablement or training sessions is a plus
What You'll Do.
Build and evolve an internal AI platform
Partner with cross-functional teams to identify needs
Translate needs into scalable AI solutions
Own and execute a clear product roadmap
Ensure consistent and predictable delivery of features
Develop integrations between AI systems and internal tools
Design reusable AI patterns and workflows
Replace fragmented solutions with scalable platform capabilities
Lead internal enablement efforts
Conduct training sessions
Manage AI adoption programs
Contribute to AI governance policies
Contribute to AI security policies
Contribute to AI usage policies
Ensure safe and compliant use of AI systems
Continuously refine and improve internal AI tools
Incorporate user feedback
Adapt to evolving organizational needs
How You'll Work.
Team & Collaboration
Cross-functional teams; Internal stakeholders
Communication Scope
Translate workflows
Process & Methodology
Roadmap management
Full Job Description
## Accountabilities Build and evolve an internal AI platform composed of reusable tools, integrations, workflows, and AI-enabled systems that enhance productivity across all teams Partner with cross-functional teams (Sales, CS, Marketing, Finance, Legal, HR, Security, Engineering) to identify needs and translate them into scalable AI solutions Own and execute a clear product roadmap for internal AI capabilities, ensuring consistent and predictable delivery of features and improvements Develop integrations between AI systems and internal tools using APIs, webhooks, MCP connectors, and automation frameworks Design reusable AI patterns and workflows that replace fragmented, one-off solutions with scalable platform capabilities Lead internal enablement efforts including training sessions, documentation, onboarding, and AI adoption programs across the company Contribute to AI governance, security, and usage policies to ensure safe and compliant use of AI systems Continuously refine and improve internal AI tools based on user feedback and evolving organizational needs Requirements: 5+ years of experience in software engineering, platform engineering, or AI/automation-focused development roles Strong hands-on experience with AI systems, particularly Anthropic Claude or similar LLM-based tools, including building real production use cases Proficiency in Python and experience working with REST APIs, webhooks, JSON, and system integrations Demonstrated ability to design reusable systems and scalable architectures rather than isolated solutions Experience building internal tools, automation workflows, or platform-level engineering solutions Strong understanding of secure software development practices, secrets management, and data protection principles Awareness of AI security risks such as prompt injection, data leakage, and misuse of LLM outputs Excellent communication skills with the ability to translate non-technical workflows into AI-driven solutions Ability to operate in
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