Prompt Engineering

Unify Technologies

Detailed Job Description:

Project Overview:

  • Objective: The primary objective is to develop a reusable tool tailored for prompt improvement and validation with a strong emphasis on objective performance assessment.
  • Foundation: The project will capitalize on our existing prompt engineering tools and harness the potential of our well-established cloud infrastructure to ensure efficiency and scalability.
  • Collaboration: The role of Senior Software Engineer will entail close collaboration with internal staff to align tool development with team goals and overarching objectives.
  • Initiation Phase: This position is pivotal in launching the development process. While we embark on this journey, we are actively finalising the comprehensive project plan.

Key Responsibilities:

  • Tool Development: As the lead developer, you will spearhead the creation of a robust and highly reusable tool designed to enhance and validate prompts effectively.
  • Integration: Seamlessly integrate the tool with our existing cloud infrastructure, ensuring a harmonious and efficient workflow.
  • Objective Assessment: Implement advanced features for the objective evaluation of prompts and the performance assessment of LLM’s.
  • Should have familiarity with BLEU (Bilingual Evaluation Understudy): BLEU is a metric commonly used for machine translation tasks. It measures the similarity between the generated text and human-generated reference text. A higher BLEU score indicates better translation quality.
  • ROUGE (Recall-Oriented Understudy for Gisting Evaluation): ROUGE evaluates the quality of summaries and text generation by comparing the overlap between the generated text and reference text in terms of n-grams (word sequences). It is often used for summarisation tasks.
  • Perplexity: Perplexity measures how well a language model predicts a given dataset. Lower perplexity values indicate better model performance in terms of predicting the dataset.
  • F1-Score: F1-score is a metric used for tasks like text classification and named entity recognition. It balances precision and recall, providing a single measure of model performance.
  • Human Evaluation: In some cases, human evaluators are involved in assessing the quality of LM outputs. They can rate the generated text based on factors like fluency, relevance, and overall quality.
  • Documentation: Develop comprehensive documentation to serve as a valuable resource for both end-users and team members, ensuring smooth adoption and effective collaboration.
  • Collaboration: Foster a collaborative environment by working closely with internal staff and cross-functional teams. Ensure that tool development aligns with project goals and objectives.
  • Feedback Integration: Continuously improve and iterate on the tool’s development based on user feedback and evolving requirements.


Must Have:

  • LLM Experience: A proven track record of working with Language Models, particularly LLaMa or GPT, is essential to effectively understand their nuances and challenges.
  • Instruction-Based Prompt Engineering: A deep understanding of instruction-based prompt engineering is crucial for the development of effective prompts.
  • Evaluation of Language Model Outputs: Proficiency in assessing language model outputs and utilizing relevant metrics for performance evaluation.
  • Programming Skills: Proficiency in Python and Rust is necessary for developing a robust and efficient tool.

Nice to Have:

  • Cloud Technology Familiarity: An understanding of cloud infrastructure is beneficial for streamlined integration and scalability.
  • Data Annotation Experience: Prior experience with data annotation, especially for NLP tasks, can enhance the tool’s capabilities.
  • NLP Knowledge: Familiarity with Natural Language Processing concepts and technologies provides valuable context for tool development.
  • Team Collaboration: Strong teamwork and communication skills are essential for effective collaboration with internal staff and teams.
  • Technical Skills: Familiarity with relevant tools and technologies used for data and prompt analysis adds depth to your capabilities.

Tech Stack and Libraries:

  • Programming Languages: Proficiency in Python and Rust for tool development.
  • Cloud Technologies: Familiarity with cloud platforms and services (e.g., AWS, Azure, Google Cloud) for efficient integration. NLP Libraries: Experience with NLP libraries and frameworks such as spaCy, NLTK, Transformers, or Hugging Face.
  • Web Development: Knowledge of web development technologies (e.g., HTML, CSS, JavaScript) for creating user-friendly interfaces.
  • Database Systems: Familiarity with database systems (e.g., SQL, NoSQL) for data storage and retrieval.
  • Version Control: Experience with version control systems (e.g., Git) for collaborative development.

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