Prompt Engineer

Work Location:
Toronto, Ontario, Canada

Hours:
37.5

Line of Business:
Analytics, Insights, & Artificial Intelligence

Pay Details:
$76,800 - $115,200 CAD

This role is eligible for a discretionary variable compensation award that considers business and individual performance.

Job Description:

As part of the AI Platform, our passion is to advance AI capabilities and deliver AI-powered solutions to optimize processes, maximize productivity, and increase value for our customers and colleagues. We are seeking a highly skilled and experienced Prompt Engineer to join our team and drive the development of cutting-edge AI capabilities and solutions on the Azure AI Platform. In this role, you will be at the forefront of designing, implementing, and maintaining prompts for AI models specifically tailored to support our ambitious AI initiatives. Your expertise will help shape the direction of our AI development practices and ensure that our AI solutions meet the highest standards of quality and performance.

Key Responsibilities:

1. Prompt Design and Development:
   Crafting effective prompts that elicit the desired responses from AI models. Experimenting with different phrasing and structures to optimize model output. Ensuring prompts are clear, concise, and contextually appropriate for the target audience or application.
2. Data Analysis and Evaluation:
   Analyzing model outputs to assess the effectiveness of different prompts. Conducting A/B testing and other evaluation methods to compare prompt performance. Identifying patterns and trends in AI responses to refine prompt strategies.
3. User Interaction Design:
   Designing prompts that enhance user experience in conversational interfaces. Ensuring prompts align with user intents and expectations. Collaborating with UX/UI designers to integrate prompts seamlessly into applications.
4. Model Fine-Tuning:
   Collaborating with data scientists and machine learning engineers to fine-tune AI models based on prompt performance. Adjusting prompts to improve model accuracy, relevance, and coherence. Using feedback loops to iteratively improve prompts and model responses.
5. Documentation and Knowledge Sharing:
   Documenting best practices, guidelines, and methodologies for prompt engineering. Sharing insights and findings with cross-functional teams to inform broader AI development efforts. Contributing to internal knowledge bases and training materials.
6. Ethical Considerations:
   Ensuring prompts are designed to avoid bias, inappropriate content, and other ethical issues. Staying informed about ethical guidelines and industry standards related to AI and prompt engineering. Implementing measures to mitigate potential misuse or harmful effects of AI-generated content.
7. Collaboration and Communication:
   Working closely with product managers, developers, linguists, and other stakeholders to align prompt engineering efforts with project goals. Communicating complex technical concepts to non-technical team members. Participating in team meetings, brainstorming sessions, and collaborative projects.
8. Continuous Learning and Improvement:
   Keeping up to date with the latest advancements in AI, NLP, and prompt engineering techniques. Attending workshops and training sessions to enhance skills and knowledge. Experimenting with new tools and technologies to improve prompt engineering practices.

Education/Experience:

1. Bachelor's or Master's degree in computer science, Engineering, Data Science, or a related field.
2. 2+ years' experience in designing, testing, and optimizing prompts to improve output quality, strengthen prompt security, model performance, and reduce cost.
3. Experience with RAG, various document processing & chunking.
4. Experience with programming languages such as Python, and familiarity with NLP libraries and frameworks, e.g. TensorFlow, PyTorch, SpaCy, HuggingFace Transformers, PromptFlow, LangChain, Semantic Kernel.
5. Experience with a myriad of LLM, i.e. GPT, LLama, Mistral, Gemini, Claude, Bloom, Phi.
6. Experience with vector databases and various ANN search algorithms.
7. Experience with integrating LLM into applications via APIs.
8. Experience with DevOps practice, version control, GitHub actions, Linux.
9. Excellent analytical and problem-solving skills, with a keen attention to detail.
10. Strong communication and collaboration skills to work effectively in a team environment.
11. Experience with cloud technologies, preferably Azure.