Key Responsibilities:
- Develop and deploy Slack-based AI assistants leveraging Gemini models.
- Design and implement prompt templates tailored to enterprise data use cases (Looker and Confluence).
- Establish and manage an embedding pipeline for Confluence documentation.
- Build and maintain orchestration logic for prompt execution and data retrieval.
- Set up API authentication and role-based access controls for integrated systems.
- Connect and validate vector store operations (e.g., Pinecone, Weaviate, or Snowflake vector extension).
- Contribute to documentation, internal walkthroughs, and user acceptance testing planning.
- Participate in Agile ceremonies including daily standups and sprint demos.
Required Qualifications:
- Proven experience with Gemini and large language model deployment in production environments.
- Proficiency in Python, orchestration tools, and prompt engineering techniques.
- Familiarity with vector database technologies and embedding workflows.
- Experience integrating APIs for data platforms such as Looker and Confluence.
- Strong understanding of access control frameworks and enterprise-grade authentication.
- Demonstrated success in Agile, sprint-based project environments.
Preferred Qualifications:
- Experience working with Slack app development and deployment.
- Background in MLOps, LLMOps, or AI system orchestration at scale.
- Excellent communication skills and ability to work in cross-functional teams.
