Prompt Engineer

Akaike Technologies

We are seeking an experienced and highly skilled Prompt Engineer with expertise in leveraging LLMs for various business use cases.

The successful candidate will have a strong background in NLP, an understanding of LLMs, and the ability to work closely with cross-functional teams to develop innovative solutions for our clients.


  • Work closely with product managers, data scientists, and engineers to design and develop AI-powered solutions utilizing LLMs for various business use cases.
  • Develop, test, and optimize prompt engineering techniques to harness the capabilities of LLMs effectively for a wide range of applications
  • Stay up-to-date with the latest research advancements in prompt engineering techniques.
  • Communicate with stakeholders to understand their requirements and translate them into technical specifications for AI-driven solutions.
  • Continuously evaluate the performance of implemented solutions and provide insights for improvements and enhancements.
  • Document and present findings, progress, and results to both technical and non-technical stakeholders.

Required Skills & Qualifications:

  • Experience working with LLMs, such as GPT-3/4, or similar models.
  • Proficiency in Python and experience with NLP libraries such as Hugging Face Transformers, NLTK, and SpaCy.
  • Familiarity with machine learning frameworks, such as TensorFlow or PyTorch.
  • Excellent analytical and problem-solving skills, with the ability to understand complex business problems and develop effective AI-powered solutions.
  • Strong communication and collaboration skills, with the ability to work effectively with cross-functional teams and present complex technical concepts to non-technical stakeholders.
  • Proficiency in integrating APIs, such as OpenAI’s GPT-3, to access and utilize LLMs in various business applications.

Preferred Skills:

  • Knowledge of the LangChain like frameworks is essential for structuring prompts, organizing them in a hierarchical manner, and creating reusable prompt templates.
  • Knowledge on techniques for handling token limitations using memory management concepts like vector databases and embeddings.

Prompt Design: Ability to design effective prompts that elicit desired responses from LLMs, considering aspects like context, tone, and clarity. Familiarity with prompting concepts like Zero shot, One Shot, Few shot prompting, Chain of thought prompting, auto-prompt engineering, self-reflection.

Version Control: Familiarity with version control systems like Git for managing and tracking changes to prompts and other code.

  • Experience with constructing datasets for fine-tuning LLMs efficiently for specific tasks or domains.
  • Familiarity with the software development life cycle (SDLC) and agile methodologies.
  • Knowledge of cloud platforms such as AWS, GCP, or Azure and experience with deploying AI models in production environments.
  • Hands-on coding experience using Python.

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