Data Scientist: LLMs & Production ML Systems

Qogita; Greater London; Hybrid; GBP 72,000 - 90,000; Full time

Benefits offered by this job

Equity package

Pension contributions

Learning budget

Hybrid work

Dog-friendly office

Home-office setup

Company-wide offsite

Annual leave 26 days

Performance bonus

Job summary

Qogita is seeking a data scientist with broad analytical and ML experience, including production LLM expertise. You will own the full spectrum of data science work from modelling and forecasting to LLM-powered features, collaborating with Product, Engineering and Commercial teams to power Qogita's marketplace.

You will build end-to-end ML systems in production, advise on model selection and evaluation, design RAG pipelines, and ensure reliable, observable deployments.

Qualifications

• 3+ years as a data scientist or applied ML engineer.
• Experience delivering ML systems in production with monitoring and maintenance.
• Strong Python and SQL skills; proficient with transformer models and LLMs.

Responsibilities

• Develop ML solutions across the stack: predictive models, ranking, forecasting, and LLM-powered features.
• Own ML systems end-to-end from design to deployment and monitoring.
• Advise on LLM architecture, prompt engineering, fine-tuning and evaluation.
• Design RAG architectures and evaluation frameworks.
• Collaborate with Product and Engineering to ship reliable ML products.
• Communicate model choices and trade-offs to non-technical stakeholders.

Skills

Python

SQL

LLMs

ML Engineering

RAG pipelines

LangChain

XGBoost

PyTorch

Hugging Face

Airflow

MLOps

Cloud (AWS/GCP/Azure)

Tools

LangChain

XGBoost

PyTorch

Hugging Face Transformers

Airflow

Job description

Qogita is seeking a data scientist with broad analytical and ML experience, including production LLM expertise. You will own the full spectrum of data science work from modelling and forecasting to LLM-powered features, collaborating with Product, Engineering and Commercial teams to power Qogita's marketplace.

You will build end-to-end ML systems in production, advise on model selection and evaluation, design RAG pipelines, and ensure reliable, observable deployments.