Sr. AI Enablement Engineer
New
J
JobgetherAI / Information Technology
Based in the United StatesFull-TimeSenior
Salary133,500 - 200,300 USD per year
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Job Details
- Experience
- 5+ years of software engineering, integration engineering, or related technical experience
- Required Skills
- OAuthCI/CDRESTful APIsDevOpsSoftware EngineeringLLM
Requirements
- 5+ years of software engineering, integration engineering, or related technical experience.
- 2+ years of experience building integrations between SaaS platforms, enterprise applications, or internal business systems.
- Strong experience with API integrations, OAuth, identity management, webhook architectures, and enterprise system connectivity.
- Practical experience developing and deploying LLM-based applications, AI workflows, agent systems, retrieval solutions, or model API integrations.
- Familiarity with Model Context Protocol (MCP) or comparable agent-to-tool integration frameworks.
- Strong understanding of DevOps practices, including CI/CD, infrastructure-as-code, secrets management, and observability.
- Experience applying data governance, security, and privacy best practices in technical environments.
- Ability to evaluate third-party technology vendors, assess risks, review data flows, and provide clear recommendations to business stakeholders.
- Strong communication and collaboration skills with the ability to work effectively with technical and non-technical teams.
Responsibilities
- Design, build, and operate AI-powered workflows that improve productivity across business functions, including Finance, People Operations, Legal, and Workplace.
- Partner with stakeholders to identify high-impact workflows, translate business challenges into technical solutions, and deliver scalable AI-enabled processes.
- Own the development and governance of internal AI tools, including publishing processes, usage guidelines, review workflows, and adoption monitoring.
- Build, maintain, and scale integrations between enterprise systems using APIs, connectors, and agent-based tooling patterns.
- Develop and manage connector and Model Context Protocol (MCP) integration strategies across platforms.
- Evaluate emerging AI technologies, agent frameworks, and automation capabilities to determine adoption opportunities.
- Create technical prototypes, reference architectures, and reusable solutions that enable teams to extend AI workflows independently.
- Establish AI security, privacy, and governance practices in partnership with Security, Privacy, and Legal teams.
- Implement monitoring, observability, and operational practices to ensure AI tools remain reliable, secure, and scalable.
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