AI Architect – Agentic Systems (LLM & Multi-Agent Solutions)
Tasks
- Coordinate integration with data platform and engineering teams
- Define memory strategies for agents
- Define model cost security privacy guardrails
- Design multi-agent architectures
- Establish observability and tracing for agents
- Implement feedback loops for agent behavior
- 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 program
- Flexible working hours
- Hybrid work
- Referral bonus
- Training
- Workplace wellbeing programs
Skills/Tech-stack
A2A | AI Foundry | AP2 | APIs | AWS | Agent coordination | Agent coordination patterns | Agent systems | Amazon Bedrock | Azure | Azure AI | Azure AI Foundry | Context Windowing | Coordination Patterns | Distributed Systems | Event Driven | Event-driven architecture | Fine Tuning | Function Calling | GCP | Google Vertex | Google Vertex AI | Hallucination mitigation | Instruction Tuning | LLM orchestration | Language Models | Large Language Models | MCP | Memory Management | Multi-Agent | Multi-Agent Systems | Observability | Pretraining | Python | RAG | Retrieval Quality | Retrieval optimization | Retrieval-Augmented Generation | Semantic similarity | Software Architecture | Task decomposition | Text Embeddings | Tokenization | Tool use | Transformer | UCP | Vector Database | Vector Representations | Vertex AI
Education
N/A
Roles
Regions
Countries
States
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