• Punët
• Rreth nesh
• Për punëdhënësit

3. Make rules and knowledge maintainable

• Design a clear separation between operational facts, business policies and software logic.
• Work with named business owners to define rule precedence, exceptions, validation and approval.
• Keep draft or inferred rules separate from approved rules.
• Support versioned updates without a software release, with historical replay, traceable approval and rollback.
• Use grounded reasoning and human escalation where rules do not resolve a case.

4. Own engineering quality and evaluation

• Review and test code, including AI-generated code, against the agreed architecture.
• Establish automated CI checks, integration tests and representative operational cases.
• Track the exact application, rule and data versions evaluated.
• Implement decision logs, monitoring, access controls and failure recovery.
• Define test-readiness evidence and agree workflow-quality, latency and operating-cost measures with the team and client.

5. Keep technical decisions and progress clear

• Maintain a concise architecture and decision record that shows what works, what remains and why.
• Raise blockers early with evidence, an owner, a decision needed and delivery impact.
• Propose simpler alternatives and update estimates when assumptions change.
• Demonstrate working increments, and clearly distinguish prototypes from integrated functionality.
• Leave the team with maintainable code, documentation and a practical handover.

What good looks like

• Before build commitments: a reviewed technical baseline, verified critical dependencies and explicit assumptions behind the estimate.
• During delivery: a working end-to-end increment, automated tests running in CI, and demonstrated progress against agreed acceptance cases.
• For rule changes: a named business approver, a versioned baseline and recorded replay results before promotion.
• At evaluation: reproducible results, decision traces and documented limitations that support the agreed Go/No-Go decision, not just a successful demo.

Specific targets will be agreed after the baseline is established. You will stay involved from the initial working sessions through delivery and evaluation.

What you bring

• Evidence of taking an applied AI or LLM-enabled workflow beyond a prototype into operational use.
• Strong hands-on software engineering, architecture, API integration and code-review skills.
• Experience turning incomplete or conflicting discovery material into implementable, testable specifications.
• Sound judgment about structured rules, retrieval, AI reasoning and human review, including their failure modes.
• Practical experience with evaluation datasets, regression testing, CI and production observability.
• Confidence to challenge assumptions, explain alternatives and escalate decisions constructively across business and engineering teams.
• Clear, concise communication and realistic estimation based on verified dependencies and available capacity.

Nice to have

• Experience in exception-heavy operations, business-rule systems, travel, hospitality or logistics
• Familiarity with AWS-based AI deployments