AI Architect – Agentic Systems (LLM & Multi-Agent Solutions)
Tasks
- Define memory strategies for agents
- Define model cost security privacy guardrails
- Design multi-agent architectures
- Establish observability and tracing for agents
- Evaluate and improve LLM reliability and accuracy
- Guide teams on best practices
- Implement agent systems from PoC to production
- Implement feedback loops and controls for agent behavior
- Integrate AI into core enterprise systems
- Integrate LLM agents with APIs and tools
- Integrate LLM agents with enterprise data platforms
- Orchestrate agent workflows
Perks/Benefits
- Career coaching
- Conferences
- Employee assistance programme
- Flexible working hours
- Hybrid work
- Referral bonus
- Training
- Wellbeing programme
Skills/Tech-stack
AI Foundry | API Integration | AWS | Agent Orchestration | Agent coordination | Agent systems | Amazon Bedrock | Azure | Azure AI | Azure AI Foundry | Context Management | Distributed Systems | Driven systems | Event Driven | Event Driven Systems | Feedback loops | Function Calling | GCP | Google Vertex | Google Vertex AI | Information Retrieval | Language Models | Large Language Models | Machine Learning | Memory Management | Multi-Agent | Multi-Agent Systems | Observability | Python | RAG | Software Architecture | Text Embeddings | Tokenization | Tool use | Tracing | Transformer Architecture | Vector Databases | Vertex AI
Education
N/A
Roles
AI | AI Architect | Architect
Regions
Countries
States
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