Role mission
The AI Engineering Advisor helps Trafin implement the most modern AI practices, models, skills and agentic workflows so the company stays among the technology leaders in the practical use of AI.
This is not a consultant for presentations, a trainer, or an adoption person.
It is an engineer who builds AI-native systems and shows concrete implementations:
This is what we deployed at another company. Here is the skill. Here is the benchmark. Here is the cost saving. Here is the agent. Here is the implementation.
Position in the organization
The only external role that stands beside the CTO as their specialized partner for AI excellence.
- Not part of delivery.
- Not part of management.
- Helps the Product Builder keep a technological lead over the market.
Main responsibilities
AI Systems & Architecture
- designing AI-native systems
- architecture of agentic workflows
- integrating models into products and operations
Agents & Skills
- designing and implementing agents
- creating and sharing skills
- orchestrating multiple agents and tools
Context Engineering
- designing context and knowledge layers
- RAG and retrieval strategies
- connecting to Business As Code for both people and AI
Evaluation & Benchmarking
- evaluating models, agents and workflows
- benchmarks against the best in the field
- regression and qualitative tests of AI outputs
Cost Optimization
- choosing the right models for a given task
- optimizing inference costs
- measuring the price/performance ratio
Capability Transfer
- transferring proven implementations from other companies
- concrete skills, agents and benchmarks
- quickly introducing state-of-the-art techniques
AI-First working style
The role is AI-native by principle.
It works with top-tier models and tools and actively tracks the frontier of the field:
- Claude
- Codex
- Claude CLI
- AI agents, skills and orchestration
- evaluation and observability tools
How we know you are successful
- the company keeps a technological lead in the practical use of AI
- production agents and skills run with measurable impact
- AI costs are optimized
- AI output quality is measured and evaluated
- the latest practices reach the team quickly
Working with other roles
CTO & Product Builder
The main partner. Together they keep the technological lead and bring AI into products and architecture.
System Owner
Cooperation on evaluation, quality, monitoring and Business As Code.
The whole IT team
Shares concrete implementations, skills and benchmarks at the AI Engineering Session.