AI Engineer

Data Scientist/AI Engineer – Generative AI Location: Remote (UK or Europe) Engagement: Contract (Outside IR35) Duration: 6+ Months A leading consultancy client is seeking a highly skilled AI Engineer with expertise in NLP and Gen AI to support a range of cutting-edge projects across internal R&D and enterprise client delivery. This is an exciting opportunity to work on impactful initiatives involving LLMs, RAG pipelines, agentic AI, and modern MLOps practices. The ideal candidate will have experience developing and deploying intelligent AI solutions and working in dynamic, fast-paced environments. Experience with Databricks is needed in this position. Key Responsibilities • Translate business and technical requirements into scalable NLP/GenAI solutions (e.g., document processing, intelligent search, summarisation, Q&A systems, workflow automation). • Conduct research and prototyping around the latest LLM technologies, RAG pipelines, and tool-using agent architectures. • Design and build AI systems that integrate multi-step reasoning and LLM-based interactions. • Develop lightweight user interfaces using Streamlit, Flask, or similar frameworks to support internal demos and client-facing pilots. • Apply MLOps best practices including MLflow tracking, Docker-based packaging, and CI/CD workflows for seamless deployment. • Deliver rapid prototypes and client-ready solutions using the Databricks MosaicAI suite. • Contribute to both experimental proof-of-concepts and scalable production-grade AI deployments. Candidate Requirements • 4+ years of hands-on experience in machine learning, with a strong focus on NLP and GenAI applications. • Proven experience delivering ML or deep learning models in real-world environments. • Proficient in Python, with strong familiarity with NLP/LLM tools such as: HuggingFace Transformers, LangChain, LangGraph, LlamaIndex, etc. • Prompt engineering and fine-tuning LLMs, Chunking strategies, vector databases, and semantic search, Retrieval-augmented generation (RAG) workflows • Solid grounding in statistical methods, model diagnostics, evaluation, and interpretability. • Strong knowledge of MLOps tooling and practices:Experiment tracking with MLflow, Containerization using Docker, CI/CD for ML pipelines, Monitoring and retraining workflows • Hands-on experience with Databricks for model development and deployment.