Gen AI Engineer

New

InnodataGenerative AI

Remote - CanadaFull-TimeJunior

Salary80,000 - 90,000 CAD per year

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Job Details

Experience: 2 years of prompt engineering / LLM fine-tuning, or related AI/ML roles.
Required Skills: PythonPyTorchTensorflowPrompt Engineering

Have 2 years of prompt engineering, LLM fine-tuning, or related AI/ML experience.
Have familiarity with annotation and human-in-the-loop workflow tools or platforms, such as Labelbox.
Have experience designing and automating data annotation workflows.
Understand data annotation and the challenges of scaling human-in-the-loop workflows.
Be familiar with cloud platforms, containerization, and model deployment.
Have a deep understanding of LLMs, including transformer-based architectures.
Have experience programmatically using LLMs for data labeling, classification, localization, and annotation tasks.
Have strong Python expertise for NLU, data processing and transformation, and statistical analysis.
Be familiar with JSON, JavaScript, or XML.
Have experience with AI/ML frameworks and libraries such as TensorFlow, PyTorch, and Jupyter.
Be familiar with APIs and platforms for working with LLMs, such as OpenAI and Hugging Face.
Understand localization best practices and cultural nuances across languages and regions.
Understand LLM evaluation metrics and how to assess model reliability, bias, and generalizability.
Have experience with data pipelines, automation tools, and integrating models into production systems.

Collaborate with data scientists, linguists, and localization experts to support accuracy and cultural relevance.
Prototype and validate AI models to assess feasibility, potential impact, and effectiveness.
Design, develop, and implement prompts for data labeling and localization in software applications.
Use knowledge of software components, data structures, data formats, and data modeling to iterate on solutions.
Conduct user testing and analyze feedback to optimize prompt design for data accuracy and linguistic consistency.
Analyze model performance using KPIs and metrics, and assess whether models meet 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 data annotation systems.
Create guidelines and training materials for prompt use in data labeling and localization projects.
Monitor data labeling and localization trends and tools to inform prompt engineering techniques.

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