AI/ML Architect (Generative AI) - HCM
Tasks
- Define agentic AI reference architecture
- Define data control and compliance approach
- Define model governance standards
- Design RAG systems
- Design human-in-the-loop workflows
- Design multi-agent orchestration
- Establish evaluation methodology and quality gates
- Guide engineering teams on runtime and platform architecture
- Implement model versioning and traceability
- Make build-vs-buy decisions
- Select cloud and on premise execution architecture
- Set security and audit logging standards
- Translate customer use cases into scalable reference designs
Perks/Benefits
- Collaborative work environment
- Competitive benefits package
- Continuous learning
- Cutting-edge projects
- Professional growth
Skills/Tech-stack
AWS Bedrock | Agent Orchestration | Agent/tool use | Amazon SageMaker | Audit Logging | Azure AI | CI/CD | ChromaDB | Cloud Native | Cloud Native Architecture | Data Governance | GRPC | Generative AI | Gguf | Google Vertex | Google Vertex AI | Human-in-the-loop | Langchain | Language Models | Large Language Models | Llama CPP | Llamaindex | Machine Learning | Microservices | Model Context Protocol | Model Governance | Multi-Agent | Multi-agent orchestration | OpenSearch | Pinecone | Qdrant | RAG evaluation | REST API | Retrieval-Augmented Generation | Security Architecture | The Loop | Tool use | VLLM | Vector Databases | Vertex AI | WebSocket
Education
N/A
Roles
AI | AI Architect | AI/ML Architect | Architect | Generative AI Architect | ML Architect
Regions
Countries
States
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