Puritas Group

Data Scientist & 2x Machine Learning Engineers

⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Data Scientist and 2x Machine Learning Engineers, offering a 12-month contract at an inside IR35 rate. Candidates must have recent insurance industry experience. Key skills include AI, ML, ETL, Docker, and Kubernetes. Hybrid work model.
🌎 - Country
United Kingdom
💱 - Currency
£ GBP
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💰 - Day rate
Unknown
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🗓️ - Date
May 13, 2026
🕒 - Duration
More than 6 months
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🏝️ - Location
Hybrid
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📄 - Contract
Inside IR35
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🔒 - Security
Unknown
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📍 - Location detailed
London Area, United Kingdom
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🧠 - Skills detailed
#Docker #Scala #Cloud #ML (Machine Learning) #Deployment #Monitoring #AI (Artificial Intelligence) #Automation #"ETL (Extract #Transform #Load)" #Data Science #Kubernetes
Role description
Data Scientist & 2x Senior ML Engineers - London Insurer Supporting a major AI transformation programme for a leading London Insurer. Candidates must have recent experience working in the Insurance Industry. 12 month Contract Inside IR35 Hybrid - 2 days per week onsite I’m partnering with a client who is scaling their AI capability and looking for a Data Scientists and Senior/Lead ML Engineers to join a high‑impact programme focused on building, deploying, and operationalising advanced AI and LLM‑driven solutions. Data Scientist responsibilities include: • Translating business problems into technical AI requirements • Designing training strategies and selecting ML/LLM architectures • Data acquisition, preprocessing, and feature preparation • Evaluating and optimising models (accuracy, precision, recall, F1, etc.) • Prompt engineering and LLM interaction design • Supporting deployment, monitoring, and performance reporting • Communicating insights to stakeholders and cross‑functional teams ML Engineer responsibilities include: • Designing and implementing AI/LLM architectures and pipelines • Building robust ETL/ELT workflows and integrating structured/unstructured data • Fine‑tuning LLMs (LoRA, QLoRA), building RAG pipelines, and using vector DBs • Developing agentic systems and connecting LLMs to APIs and external tools • Deploying models into production with CI/CD automation • Monitoring performance, reliability, and cost efficiency • Managing infrastructure (cloud, Docker, Kubernetes) • Ensuring responsible AI, governance, versioning, and reproducibility These roles sit at the intersection of advanced modelling, LLM engineering, and production‑grade AI delivery. Ideal for specialists who enjoy working end‑to‑end across the model lifecycle and contributing to a modern, scalable AI ecosystem. If you’re interested in exploring this opportunity, feel free to reach out.