AI Engineer
Job Description
Build shared AI enablement capabilities that help teams adopt AI faster through reusable skills, agents, templates, and documented workflows.
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 for consistent adoption.
- Own the intake, prioritization, and tracking of AI enablement requests.
- Support AI coaches by packaging and scaling successful team solutions into shared assets.
- Measure adoption, maintain asset quality, and coordinate releases.
Requirements
- Python expertise.
- GitHub.
- AWS is a plus.
- Experience with AI frameworks (example: Langchain).
- Working knowledge of MCPs.
- Experience with RAG and vector databases.
- Harness Engineering.
Technologies
- Python
- GitHub
- AWS
- Langchain
- MCPs
- RAG
- Vector Database
- Harness Engineering
- Databricks
Working Model
- Act 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, complementing 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.
Nice to Have
- Big Data (Databricks).
- Multi agent system design and development.
Location: Austin, TX (onsite)
Compensation: USD 60 - 65 per hourly
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