Prompt Engineer

InnoData

. Design and implement prompt strategies to improve accuracy, localization, and cultural alignment in data labeling and translation processes .

Posted 10/8/2026full-timeRemote • CanadaJuniorMid-Level💰 CA$80,000 - CA$90,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in prompt engineering and LLM fine-tuning, with a strong focus on automating data labeling and localization processes. Proficient in collaborating with cross-functional teams to design scalable AI-driven solutions and optimize model performance using relevant metrics.

Highest-signal resume keywords

Prompt EngineeringLLM Fine-TuningPython for NLUData Annotation WorkflowsCloud Platforms

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Prompt DesignData ProcessingStatistical AnalysisModel Evaluation MetricsData StructuresData ModelingAutomation ToolsLocalization Best PracticesAPIs for LLMsHuman-in-the-Loop Workflows

Soft Skills

CollaborationCommunicationUser TestingFeedback Analysis

Tools & Technologies

LabelboxTensorFlowPyTorchJupyterOpenAIHugging FaceJSONJavaScriptXML

Industry Keywords

AIMachine LearningData LabelingLocalizationCultural Alignment

Tech Stack

Tools & technologies

CloudJavaScriptPythonPyTorchTensorflow

About the role

Key responsibilities & impact

Design and implement prompt strategies to improve accuracy, localization, and cultural alignment in data labeling and translation processes
Translate business requirements into scalable AI-driven solutions with Product, Data Science, Operations, and client stakeholders
Identify automation opportunities and develop prompt-based workflows
Continuously measure and refine performance to ensure quality and reliability
Collaborate with data scientists, linguists, and localization experts
Prototype and validate AI models
Design, develop, and implement prompts for data labeling and localization within software applications
Understand software stack components, use cases, data structures, data formats, and data modeling to iterate on solutions
Conduct user testing and feedback analysis to optimize prompt design
Analyze model performance using KPIs and metrics against customer acceptance criteria
Communicate technical findings and solution strategies to technical and non-technical stakeholders
Collaborate on data pipelines and workflows integrating LLMs into automated systems
Create guidelines and training materials for prompt usage
Monitor industry trends and tools in data labeling and localization

Requirements

What you’ll need

2 years of prompt engineering / LLM fine-tuning, or related AI/ML roles
Familiarity with tools/platforms for annotation and human-in-the-loop workflows (e.g., Labelbox)
Experience designing and automating data annotation workflows
Knowledge of data annotation and the challenges of scaling human-in-the-loop workflows
Familiarity with cloud platforms, containerization, and model deployment
Deep understanding of LLMs, including transformer-based architectures
Demonstrated experience programmatically using LLMs to automate data labeling, classification, localization and annotation tasks
Strong expertise in Python for NLU, data processing and transformation, and statistical analysis
Familiarity with JSON, Javascript or XML
Experience with TensorFlow, PyTorch, Jupyter, and other relevant AI/ML tools
Familiarity with APIs and platforms for working with LLMs, such as OpenAI and Hugging Face
Knowledge of localization best practices and cultural nuances for different languages and regions
Strong understanding of LLM evaluation metrics and ability to assess model reliability, bias, and generalizability
Experience working with data pipelines, automation tools, and integrating models into production systems