AI Enablement Lead [gn] Data Intelligence
Tasks
- Apply security controls and privacy standards
- Build AI orchestration layers
- Build embedding and caching strategies
- Create reusable AI frameworks
- Define standards for AI tooling
- Design and maintain internal AI platforms
- Design semantic search solutions with vector databases
- Develop API gateways for AI solutions
- Establish LLMOps and MLOps practices
- Evaluate AI technologies and vendors
- Implement model governance and evaluation
- Integrate generative AI into data platform solutions
- Lead AI enablement workshops and documentation
- Manage model costs token usage and latency
- Measure AI adoption success metrics
- Optimize AI infrastructure performance
- Prototype AI solutions from proof of concept to production
- Provide architecture guidance and knowledge sharing
- Set up monitoring systems
Perks/Benefits
- Career development
- Continuous learning
- Flexible work arrangements
- Hybrid work
- Professional growth
- Remote work
Skills/Tech-stack
API Development | Agentic AI | Automated testing | CI/CD | Clean architecture | Docker | Fine Tuning | Go | Kubernetes | LLMOps | Langchain | Language Models | Large Language Models | Llamaindex | MLOps | Machine Learning | Milvus | Model Evaluation | Model Governance | Monitoring | PGVector | Pinecone | Privacy | Prompt engineering | Python | RAG | Security controls | Semantic Search | Token optimization | Vector Databases
Education
N/A
Roles
AI | AI Engineer | Architect | Engineer | LLMOps Engineer | Learning Engineer | Machine Learning Engineer | Software Architect
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