AI Architect – Agentic Systems (LLM & Multi-Agent Solutions)
CAD 146K-175K (estimate) Senior-level Full Time
Tasks
- Define model cost security privacy guardrails
- Design multi-agent architectures
- Establish observability tracing feedback loops
- Evaluate LLM reliability and accuracy
- Guide teams on scalable reliable AI systems
- Implement memory strategies short term long term contextual grounding
- Integrate AI with core enterprise systems
- Integrate LLM agents with APIs and tools
- Integrate LLM agents with enterprise data platforms
- Integrate LLM agents with operational workflows
- Lead agentic systems from PoC to production
- Orchestrate agent coordination patterns
Perks/Benefits
- Career coaching
- Employee assistance programme
- Flexible working hours
- Global career opportunities
- Hybrid work
- Training
- Workplace wellbeing programme
Skills/Tech-stack
AI Foundry | API Integration | AWS | Agent coordination | Agent coordination patterns | Agent systems | Amazon Bedrock | Amazon Web Services | Azure AI | Azure AI Foundry | Cloud Architecture | Cloud platform | Context Management | Coordination Patterns | Distributed Systems | Driven systems | Embedding Models | Event Driven | Event Driven Systems | Feedback loops | Fine Tuning | Function Calling | GCP | Google Cloud | Google Cloud Platform | Google Vertex | Google Vertex AI | Hallucination mitigation | Instruction Tuning | LLM Applications | Language Models | Large Language Models | Memory Management | Microsoft Azure | Model Evaluation | Multi-Agent | Multi-Agent Systems | Observability | Orchestration | Pretraining | Privacy | Python | RAG | Retrieval optimization | Security | Semantic Search | Semantic similarity | Software Architecture | Task decomposition | Tool use | Tracing | Transformer Architecture | Transformer Tokenization | Vector Databases | Vertex AI | Web Services
Education
N/A
Roles
AI | AI Architect | Architect | Learning Architect | Machine Learning Architect
Regions
Countries
States
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