AI Engineer

VIVA USA

• $110K — $130K *
Information Technology
Less than 5 years of experience
Job Overview by Ladders

Qualifications

  • 5-7 years of experience in AI and technology development
  • Proficiency in Python programming
  • Experience with GitHub for version control
  • Familiarity with AWS services is a plus
  • Knowledge of AI frameworks, such as Langchain
  • Understanding of MCPs and RAG/Vector databases
  • Experience in harness engineering

Responsibilities

  • Build and maintain the AI Skills, Agents, and Templates Hub
  • Develop reusable AI skills, agents, workflows, and starter templates
  • Create onboarding materials, documentation, and best practices
  • Manage intake and prioritization of AI enablement requests
  • Support AI coaches in packaging and scaling successful solutions
  • Measure adoption and maintain the quality of AI assets
  • Coordinate releases for AI solutions

Benefits

  • Opportunity to work with cutting-edge AI technologies
  • Support for professional growth and skill development
  • Collaborative environment with AI coaches and leaders
  • Contribution to AI adoption across the organization
  • Impact on efficiency and knowledge sharing within teams
Full Job Description
Role Summary
Support the AI Enablement program by building and maintaining the tools, content, and processes that accelerate AI adoption across the organization. This individual will partner with AI Enablement leads and embedded AI coaches to turn successful use cases into reusable skills, agents, templates, workflows, and knowledge assets that can be shared across teams. The role aligns with the AI Enablement vision of providing reusable AI capabilities, onboarding resources, and adoption support across department.

Key Responsibilities
Build and maintain the AI Skills, Agents, and Templates Hub.
Develop reusable AI skills, agents, workflows, and starter templates.
Create onboarding material, documentation, and best practices.
Manage intake, prioritization, and tracking of AI enablement requests.
Support AI coaches in packaging and scaling successful team solutions.
Measure adoption, maintain asset quality, and coordinate releases.

Working Model
Acts as the central builder and operator for shared AI enablement capabilities.
AI coaches identify use cases, provide domain expertise, validate solutions, and drive adoption within their teams.
AI Enablement leadership sets priorities and strategic direction. This complements the embedded coach model announced across DSG.

Success Measures
Increased adoption of AI tools and resources.
Growth of reusable skills, agents, and templates.
Faster onboarding and knowledge sharing.
Reduced duplication of effort across teams.
Improved discoverability and reuse of AI solutions.

Additional Required Skills:
Python expertise
Github
AWS is a plus
AI Frameworks (ex. Langchain)
MCPs
RAG and Vector Database
Harness Engineering

Nice to have:
Big Data (Databricks)
Multi agent system design/development

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