AI Architect – Agentic Systems (LLM & Multi-Agent Solutions)
Tasks
- Collaborate with data platform and engineering teams
- Coordinate agent behavior controls
- Define model cost security privacy guardrails
- Design multi-agent architectures
- Establish observability tracing feedback loops
- Implement memory strategies
- 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 workflows
- Train teams on scalable reliable AI systems
Perks/Benefits
- Career coaching
- Employee assistance programme
- Flexible working hours
- Global career opportunities
- Hybrid work
- Internal tech communities
- Training
- Wellbeing apps access
- Wellbeing programme
- Workshops and conferences
Skills/Tech-stack
AI Foundry | API Integration | AWS | Agent Frameworks | Agent coordination | Agent coordination patterns | Amazon Bedrock | Attention Mechanisms | Azure | Azure AI | Azure AI Foundry | Cloud Computing | Context Management | Context windows | Coordination Patterns | CrewAI | Custom Agents | Distributed Systems | Driven systems | Event Driven | Event Driven Systems | Feedback loops | Fine Tuning | Function Calling | GCP | Google Vertex | Google Vertex AI | Hallucination mitigation | Instruction Tuning | LLM Accuracy | LLM Evaluation | LLM Reliability | Langchain | Language Models | Large Language Models | Memory Management | Multi-Agent | Multi-agent frameworks | Observability | Pretraining | Privacy | Python | RAG | Responsible AI | Retrieval optimization | Security | Semantic Kernel | Semantic similarity | Text Embeddings | Tokenization | Tool use | Tracing | Transformer Architecture | Vector Databases | Vertex AI
Education
N/A
Roles
AI | AI Architect | Architect
Regions
Countries
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