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Salary: PLN 174,500 - 216,000 / annual
project44 is a supply chain AI platform that transforms fragmented logistics data into real-time, AI-powered insights. The company's Decision Intelligence Platform, Movement, empowers global enterprises to connect instantly, see clearly, act decisively, and automate intelligently across supply chain operations.
The Prompt Engineer is a specialist in building, expanding, and managing AI Agents that power project44's AIA Operations organisation. Acting as both an AI workflow architect and operational strategist, you will design, govern, and scale operational frameworks that underpin AIA Operations globally. This is an individual contributor role that drives measurable operational improvement through deep domain expertise, disciplined experimentation, and cross-functional collaboration.
Key responsibilities include:
**AI Workflow Development & Prompt Engineering:**
Design, build, and maintain AI agent workflows that automate carrier monitoring, performance tracking, exception management, and escalation processes. Build custom, customer-specific AI agent workflows tailored to individual carrier, lane, or account requirements. Write, test, iterate, and optimise prompts for large language models, ensuring accuracy, reliability, and operational efficiency. Run structured tests to evaluate prompt performance, comparing variants against defined criteria and documenting outcomes. Track prompt versions and iterations, maintaining clear records of changes and reasoning behind design decisions. Identify where LLM outputs fall short and systematically diagnose and resolve issues through prompt refinement. Work closely with engineers through handover and deployment, ensuring workflows are correctly implemented at go-live. Ramp up new use cases post-launch, gradually expanding scope and surfacing edge cases as real-world volume increases. Monitor agent performance on an ongoing basis, identifying drift, degradation, or failure modes. Maintain and support existing production workflows throughout their lifecycle. Evaluate new AI tools and platforms, providing structured recommendations on adoption.
**Operational Governance:**
Continuously increase AI Agent build velocity by escalating process pain points and driving improvements. Identify opportunities to expand automation coverage and proactively bring forward structured proposals for new AI capabilities. Support continuous improvement across the full AI Agent build lifecycle.
**Cross-Functional Collaboration:**
Read and interpret Product Requirements Documents, transforming them into clear AI agent requirements. Work with Product Managers throughout the full agent build lifecycle. Work closely with engineers who build the connectors that deploy AI agents. Collaborate with external vendors to reduce friction and influence new feature releases.
**Customer Collaboration:**
Partner directly with customers to understand their operational goals and translate them into tailored AI agent workflows. Work directly with customers post-launch to review agent performance and drive iterative improvements. Serve as a subject-matter point of contact for customers on agent behaviour, troubleshooting, and enhancement requests.
**Team Enablement:**
Contribute to training resources, internal knowledge bases, and team documentation that raise the AI capability of the broader function. Champion a culture of disciplined experimentation, continuous learning, and rigorous quality standards.
The role is based in Krakow, Poland with an expectation to be on-site four days per week.
**Requirements:**
- 3-4+ years in transportation operations, logistics technology, carrier management, or network operations; global or multi-region experience preferred
- Hands-on experience working with AI tools, automation platforms, or workflow-based technologies in an operational context
- Troubleshooting experience across EDI, API, data pipelines, system configuration, and workflow orchestration
- Strong analytical skills — comfortable using data, monitoring dashboards, and structured observation to diagnose problems and measure outcomes
- Demonstrated ability to work collaboratively across Product, Engineering, AI, and Operations stakeholders
- Excellent written and verbal communication skills; able to read and interpret product requirements and translate them into clear, structured specifications for AI workflows
- A genuine curiosity about how LLMs work and a methodical approach to testing and refining prompts — patience and rigour matter more than prior formal training
- Ability to write clear, structured prompts and instructions that direct AI behaviour accurately and consistently
- Familiarity with core prompting techniques such as step-by-step instructions, examples within prompts (few-shot), and breaking complex tasks into stages
- Comfort working in AI tools and platforms to build, test, and iterate on workflows without needing to write code
- Ability to critically evaluate LLM outputs — spotting errors, inconsistencies, and failure modes — and translate observations into targeted prompt improvements
- An understanding of LLM behaviour and limitations — including how models can hallucinate, misinterpret instructions, or produce inconsistent outputs — and how to design prompts that account for these tendencies
