AI Enablement Lead [gn] Data Intelligence
Tasks
- Build reusable AI frameworks
- Create technical documentation
- Define embedding strategies
- Define model evaluation processes
- Define standards for vector databases
- Design internal AI platforms
- Drive AI prototyping to production
- Ensure scalable reliable maintainable deployments
- Establish LLMOps practices
- Establish MLOps practices
- Evaluate AI technologies vendors models
- Implement AI governance frameworks
- Implement caching mechanisms
- Implement privacy standards
- Implement security controls
- Implement semantic search approaches
- Integrate production generative AI into data solutions
- Lead AI enablement workshops
- Maintain orchestration layers
- Manage AI infrastructure performance
- Optimize model costs
- Optimize token usage
- Provide architecture guidance
- Reduce model latency
- Set up monitoring systems
- Support knowledge-sharing
Perks/Benefits
- Career development
- Continuous learning
- Flexible work arrangements
- Hybrid work
- Professional growth
- Remote work
Skills/Tech-stack
Automated testing | CI/CD | Clean architecture | Docker | Embeddings | Fine Tuning | Go | Kubernetes | LLMOps | Langchain | Language Models | Large Language Models | Llamaindex | MLOps | Machine Learning | Milvus | Model Evaluation | Monitoring | PGVector | Pinecone | Privacy | Prompt engineering | Python | RAG | Security | Semantic Search | Vector Databases
Education
N/A
Roles
AI | AI Enablement | AI Enablement Lead | Architect | Enablement Lead | Engineer | Lead | Learning Engineer | Machine Learning Engineer | Software Architect
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