Data Scientist - NLP, LLMs, & Prompt Engineering (Hiring Immediately)

Role: Data Scientist – GenAI, Python, NLP, LLMs, & Prompt Engineering

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 behavior 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 .