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- Tasks: Develop and deploy NLP solutions, engineer prompts for LLMs, and manage conversational context.
- Company: Join a cutting-edge tech company focused on AI innovation and natural language processing.
- Benefits: Enjoy remote work flexibility and competitive pay of £450/day outside IR35.
- Why this job: Be at the forefront of AI technology, collaborating with creative minds in a dynamic environment.
- Qualifications: Proven Python and NLP experience, with a strong grasp of prompt engineering and LLMs.
- Other info: Contract role for 3-6 months, perfect for those looking to make an impact in AI.
The predicted salary is between 54000 - 90000 £ per year.
Location: Remote Contract – 3-6 Months
Rate - £450/Day Outside IR35
Job Overview: We are seeking a highly skilled and creative Data Scientist with deep expertise in Python programming, Natural Language Processing (NLP), and Large Language Models (LLMs). This role demands hands-on experience in prompt engineering, designing intelligent conversational flows, managing context windows, and interfacing with APIs such as OpenAI’s Chat Completions API. The ideal candidate should be capable of designing, evaluating, and optimizing AI systems that generate high-quality, context-aware responses.
Key Responsibilities:
- Develop and deploy NLP solutions using libraries such as NLTK, SpaCy, and TextBlob.
- Engineer prompts for LLMs using zero-shot, few-shot, chain-of-thought, and meta-prompting techniques.
- Design and refine targeted prompts to drive intelligent behaviour in AI chatbots.
- Write Python functions to interface with APIs, especially OpenAI’s Chat Completions API and similar LLM platforms.
- Manage token economy and conversational context for long, multi-turn dialogues.
- Architect sequential, step-by-step task flows for complex LLM workflows.
- Evaluate and analyze AI-generated responses to iteratively improve prompt quality and outcome accuracy.
- Collaborate with product, design, and engineering teams to deploy and monitor LLM-based features.
- Conduct experiments and fine-tune prompts to enhance response relevance, coherence, and factual correctness.
Required Qualifications:
- Proven experience with Python and NLP libraries such as NLTK, SpaCy, TextBlob, or similar.
- Hands-on experience working with LLMs (e.g., OpenAI, Claude, Mistral, etc.).
- Deep understanding of prompt engineering strategies and conversational AI workflows.
- Experience building and consuming RESTful APIs.
- Strong grasp of tokenization, embedding-based memory, and context management in LLMs.
- Ability to evaluate AI outputs for quality, relevance, and consistency.
- Familiarity with version control systems (e.g., Git) and agile development practices.
Preferred Qualifications:
- Experience with LangChain, LlamaIndex, or other LLM orchestration tools.
- Background in linguistics, cognitive science, or human-computer interaction.
- Prior work in chatbot development, virtual assistants, or AI-driven user interfaces.
- Knowledge of RAG pipelines, vector databases, and semantic search.
Data Scientist – NLP, LLMs, & Prompt Engineering employer: Careerwise
Contact Detail:
Careerwise Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Scientist – NLP, LLMs, & Prompt Engineering
✨Tip Number 1
Familiarise yourself with the latest advancements in NLP and LLMs. Follow relevant blogs, attend webinars, and participate in online forums to stay updated on trends and best practices in prompt engineering.
✨Tip Number 2
Engage with the data science community by joining platforms like GitHub or Kaggle. Contributing to open-source projects or participating in competitions can showcase your skills and help you network with industry professionals.
✨Tip Number 3
Prepare for technical interviews by practising coding challenges specifically related to Python and NLP. Websites like LeetCode or HackerRank can be great resources to sharpen your problem-solving skills.
✨Tip Number 4
Demonstrate your expertise by creating a portfolio of projects that highlight your experience with LLMs and prompt engineering. This could include case studies or examples of successful AI implementations you've worked on.
We think you need these skills to ace Data Scientist – NLP, LLMs, & Prompt Engineering
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with Python, NLP libraries, and LLMs. Use specific examples of projects where you've applied prompt engineering and developed conversational AI solutions.
Craft a Strong Cover Letter: In your cover letter, express your passion for AI and data science. Mention your hands-on experience with relevant technologies and how you can contribute to the company's goals in developing intelligent NLP solutions.
Showcase Relevant Projects: Include a portfolio or links to projects that demonstrate your skills in prompt engineering and working with APIs like OpenAI’s Chat Completions API. Highlight any experiments you've conducted to improve AI response quality.
Prepare for Technical Questions: Anticipate technical questions related to NLP, LLMs, and prompt engineering during the interview process. Be ready to discuss your approach to managing context windows and token economy in conversational AI.
How to prepare for a job interview at Careerwise
✨Showcase Your Python Skills
Be prepared to discuss your experience with Python and relevant libraries like NLTK, SpaCy, and TextBlob. You might be asked to solve a coding problem or explain how you've used these tools in past projects.
✨Demonstrate Your NLP Knowledge
Familiarise yourself with key concepts in Natural Language Processing, especially those related to LLMs and prompt engineering. Be ready to explain how you would approach designing prompts for AI systems and managing conversational context.
✨Prepare for Technical Questions
Expect questions about your hands-on experience with APIs, particularly OpenAI’s Chat Completions API. Be ready to discuss how you've interfaced with APIs in previous roles and the challenges you faced.
✨Discuss Collaboration and Agile Practices
Since collaboration is key in this role, be prepared to talk about your experience working with cross-functional teams. Highlight any agile development practices you've followed and how they contributed to successful project outcomes.
