AI Enablement Lead [gn] Data Intelligence
Tasks
- Architect AI enablement strategy end to end
- Build centralized API gateways
- Create AI documentation and blueprints
- Create evaluation frameworks
- Define and enforce official AI tooling stack
- Design AI orchestration layers
- Develop reusable AI frameworks
- Drive proof of concept to production
- Ensure data privacy and security guardrails
- Evaluate AI model vendors and open source models
- Implement MLOps and LLMOps practices
- Implement semantic caching mechanisms
- Integrate generative AI features into product
- Lead cross functional AI workshops
- Optimize token usage and API compute cost
- Reduce inference latency
- Set up model monitoring and governance
- Standardize vector database and embedding strategies
- Track AI enablement operational metrics
Perks/Benefits
Skills/Tech-stack
CI/CD | Data Privacy | Deep learning | Docker | Embeddings | Evaluation datasets | Fine Tuning | Generative AI | Go | Kubernetes | LLMOps | Langchain | Language Models | Large Language Models | Llamaindex | MLOps | Machine Learning | Milvus | Model Governance | Model Monitoring | PGVector | Pinecone | Prompt engineering | Python | RAG | Security | Semantic Search | Unit Testing | Vector Database
Education
N/A
Roles
AI | AI Engineer | Engineer | LLMOps Engineer | Learning Engineer | Machine Learning Engineer
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