Data AI Engineer- London

Role overview

Responsibilities

• check_circle Design, build and maintain data ingestion and processing pipelines.
• check_circle Source, access and integrate data from multiple structured, semi-structured and unstructured sources.
• check_circle Prepare, transform and structure large volumes of data for analytics and AI-driven applications.
• check_circle Develop integrations with internal and external REST APIs.
• check_circle Work extensively with JSON, dictionaries, DataFrames and modern data formats.
• check_circle Perform data exploration and analysis to understand data quality, structure and relationships.
• check_circle Support the ingestion, preparation and optimisation of data for AI and LLM-based applications.
• check_circle Build data solutions that enable efficient retrieval, search and analysis of information.
• check_circle Contribute to the development of AI-driven analytical and decision-support platforms.
• check_circle Collaborate closely with engineers, data specialists and business stakeholders to solve complex technical problems.
• check_circle Support advanced scenario modelling and "what-if" analysis capabilities powered by modern AI technologies.
• check_circle Stay up to date with emerging trends in data engineering, AI and machine learning.

About the company

• check_circle 3–5 years' experience in Data Engineering, Software Engineering, AI Engineering or a related technical discipline.
• check_circle Strong Python development skills.
• check_circle Proven experience using pandas for data manipulation and analysis.
• check_circle Good understanding of regular expressions (regex).
• check_circle Experience working with structured, semi-structured and unstructured datasets.
• check_circle Strong experience working with JSON, Python dictionaries and modern data structures.
• check_circle Experience sourcing, accessing, analysing and interrogating large datasets from multiple sources.
• check_circle Strong understanding of data relationships and data modelling principles.
• check_circle Experience performing joins, merges and transformations across multiple datasets.
• check_circle Ability to identify patterns, trends, anomalies and data quality issues.
• check_circle Experience understanding how disparate datasets relate to one another and can be combined to generate insight.
• check_circle Comfortable exploring unfamiliar datasets, understanding underlying structures and determining how data can be leveraged for downstream analytical use cases.
• check_circle Experience converting and managing data between DataFrames, dictionaries and JSON structures.
• check_circle Experience building and maintaining data ingestion, transformation and processing pipelines.
• check_circle Experience consuming and integrating REST APIs.
• check_circle Understanding of service-based architectures and API-driven data platforms.
• check_circle Experience building APIs using frameworks such as FastAPI would be advantageous.
• check_circle Understanding of Retrieval-Augmented Generation (RAG) concepts and architectures.
• check_circle Familiarity with Large Language Models (LLMs) and modern AI ecosystems.
• check_circle Understanding of how AI systems utilise: Vector embeddings Retrieval mechanisms Semantic search Large Language Models (LLMs)
• check_circle Vector embeddings
• check_circle Retrieval mechanisms
• check_circle Semantic search
• check_circle Large Language Models (LLMs)
• check_circle Ability to understand and work within AI-enabled data ecosystems.
• check_circle Exposure to enterprise AI platforms, LLM tooling or AI-enabled applications.
• check_circle Knowledge of machine learning concepts and techniques.
• check_circle Experience with Knowledge Graphs and graph-based data modelling.
• check_circle Experience using data visualisation tools such as: Plotly Plotly Dash
• check_circle Plotly
• check_circle Plotly Dash