AI Prompt Engineer

Trax Technologies; Cebu City; On-site; PHP 600,000 - 900,000; Full time

Job summary

Trax Inc. is seeking a prompt engineer to shape AI-driven extraction and onboarding across our cloud-based platform. You will design prompts for automated interpretation of unstructured data and systematically test and tune them for accuracy and reliability.

The role requires hands-on experience with AI/LLM tools, strong analytical skills, and the ability to collaborate with cross-functional teams including automation engineers. A degree in a related field or equivalent experience is expected.

Qualifications

• Hands-on AI/LLM prompt design and testing experience.
• Ability to document corrections and performance outcomes.
• Experience diagnosing prompt configuration issues and failures.
• Familiarity with structured/unstructured data extraction concepts.

Responsibilities

• Identify, analyze, and correct prompt configuration errors.
• Design prompts for automated extraction and interpretation of unstructured data.
• Conduct validation testing and provide tuning recommendations.
• Collaborate with internal teams including RPA engineers to support AI integration.
• Provide periodic reports on configuration outcomes, accuracy, and model improvements.

Skills

LLM behavior understanding

Prompt structure & debugging

Data extraction concepts

Analytical documentation & reporting

Written & verbal communication

Cross-functional collaboration

Education

Bachelor's degree in CS/IT/DS/Linguistics or related field

Tools

Python basics

Regex

JSON concepts

API concepts

RPA exposure

Job description

Job Description:

Trax Inc. provides cloud-based solutions and services to automate and manage freight invoice auditing and payment processing for buyers and sellers of logistics services. The Trax platform enables companies to optimize supply chain performance through greater visibility into their logistics ecosystem and predictive analytics based on over one billion logistics transactions from all industries, modes, and countries. Trax operates on a global scale with offices in the U.S., Latin America, Asia and Europe.

Qualifications

Minimum

• Bachelor's degree in Computer Science, Information Technology, Data Science, Linguistics, or a related field.
• In lieu of a degree, equivalent practical experience in AI/NLP-related work will be considered.

Recommended

• Certification or formal training in Prompt Engineering, Generative AI, Machine Learning, or NLP (e.g., DeepLearning.AI, Coursera, Google/Microsoft AI certifications).
• Background in Computational Linguistics, Cognitive Science, or Software Engineering with AI specialization.

Experience

Minimum

• At least 1–2 years of hands‑on experience working with AI/LLM tools (e.g., ChatGPT, Claude, Gemini, Copilot) for prompt design, testing, or configuration.
• Experience troubleshooting or documenting technical/process errors in a structured environment.

Recommended

• 2+ years of experience specifically in prompt engineering, AI model tuning, or AI-driven data extraction projects.
• Prior exposure to Robotic Process Automation (RPA) tools and AI-triggered workflow support (e.g., UiPath, Automation Anywhere, Power Automate).
• Experience working in a BPO, shared services, or enterprise operations environment supporting AI/automation initiatives.

Skills

Must Have

• Strong understanding of LLM behavior, prompt structure, and prompt debugging/optimization techniques.
• Ability to identify, analyze, and correct prompt configuration errors against current AI model standards.
• Working knowledge of structured and unstructured data extraction concepts.
• Strong analytical, documentation, and reporting skills (able to produce logs, summaries, and performance reports).
• Good written and verbal communication skills; able to collaborate with cross‑functional and technical teams.

Nice to Have

• Familiarity with an Optimizer tool or similar AI model configuration/pattern‑tuning environment.
• Basic scripting/technical literacy (e.g., Python, JSON, Regex, API concepts).
• Exposure to RPA script maintenance and troubleshooting.
• Knowledge of AI/model performance metrics (accuracy rate, precision, consistency benchmarks).

Task Scope

Prompt Configuration and Backlog Review

• Identify, analyze, and correct prompt configuration errors from existing and backlog records.
• Ensure prompt logic and syntax conform to the latest AI model standards.
• Document corrections, resolutions, and performance outcomes.

Support AI Extraction Onboarding

• Collaborate with internal teams to enable full AI extraction capabilities across designated systems.
• Design and configure prompts for automated extraction and interpretation of unstructured data.
• Conduct validation testing and provide tuning recommendations.

Pattern Configuration and Optimization

• Develop and configure pattern logic using the Optimizer tool (or equivalent AI model configuration environment).
• Test, refine, and validate optimized prompt patterns for accuracy, consistency, and system performance.
• Provide periodic reports summarizing configuration outcomes, accuracy rates, and model improvements.

RPA Maintenance Support

• Provide technical support for RPA scripts or workflows impacted by AI integration.
• Collaborate with automation engineers to maintain and troubleshoot RPAs and AI-triggered workflows.
• Suggest automation improvements leveraging prompt engineering insights.

EXPECTED DELIVERABLES / TASK OUTPUT

Prompt Configuration Error Resolution Log

• Weekly documentation of resolved configuration errors, including before/after examples and performance verification.

AI Extraction Onboarding Support Report

• Summary of onboarding support activities, including configured prompts, extraction test results, and tuning notes upon completion of the onboarding phase.

Pattern Optimization Package

• Bi-weekly documentation of tested patterns optimized for accuracy and efficiency within the AI Optimizer environment; model accuracy at or above defined target thresholds.

RPA Maintenance Support Summary

• Documentation of RPA issues supported, resolution approach, and system verification outcomes.
• Confirmation that automation workflows function as expected post-fix.