Insight International (UK) Ltd
AI Engineer
Leeds
Posted about 16 hours ago
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Job Description
Role: AI Engineer
Location - Leeds, UK
Contract - 6+ months
Hybrid model
Background & Experience
- AI solutions development, especially agentic AI systems.
- Wanted clarity on specific projects and challenges faced.
Agent Systems & Architecture
- What specific agent API systems worked on.
- Whether the system was RAG-based or not.
- How multi-agent sequencing was designed (tone agent, punctuation agent, etc.).
- How performance of agents was evaluated (human-in-loop, RAGAS).
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Evaluation & Observability
- How performance was measured and monitored.
- Tools used for observability (Azure logs, CloudWatch equivalent).
- How hallucinations were handled and mitigated.
- Whether Judge LLM concepts were used.
Cloud & Technical Environment
- Which cloud platforms were used (Azure, AWS, GCP).
- Strength of GCP skills from an AI agent perspective.
- Observability tools across different clouds.
LLM Usage & Tokenization
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- Which LLMs were used (OpenAI GPT-4.1, later 4.5).
- Decision process for choosing OpenAI.
- Tokenization details: context window size, chunking strategy, cost implications.
- How multi-agent design impacted token usage and costs.
Challenges & Improvements
- Hallucination issues in multi-agent systems.
- What would do differently now (e.g., hybrid ML + LLM approach, context loops).
- Awareness of tools for hallucination mitigation.
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Skills
Agentic AI Systems
RAG
Multi-agent Sequencing
LLM Evaluation
Observability
Azure
GCP
AWS
OpenAI GPT-4
Tokenization
Hallucination Mitigation
Prompt Engineering
Cloud Architecture
Python
AI Agent Design
RAGAS
Location