Software Engineer - (AI Automation & LLM )

Job Title: Software Engineer - (AI Automation & LLM )

Location: London- Hybrid role

Duration: 31st December 2025- possible extension

Role:

We're looking for a skilled Data Engineer / Software Engineer to support a pioneering AI research project focused on Large Language Model (LLM) agents and intelligent automation. You'll play a central role in preparing and filtering text, image, and video datasets used to train advanced AI systems, helping to ensure data quality, relevance, and safety

Key Responsibilities

• Data Onboarding: Convert raw datasets into structured formats suitable for automated processing.
• Filtering: Apply data cleaning, quality checks, and filtering pipelines to identify and mitigate risks in datasets.
• Post-Processing: Integrate filtering results to modify original datasets, repackage them, and prepare for re-ingestion.
• Pipeline Engineering: Design, build, and maintain scalable and efficient data pipelines.
• Collaboration: Work closely with researchers and technical teams to refine processes and contribute to ongoing AI automation work.
• Writing high-quality Python code for data manipulation and processing
• Developing and running filtering logic on diverse datasets (text, image, video)
• Automating data handling tasks and reducing manual oversight through smart tooling

Essential Skills & Experience

• Strong experience with Python and data processing libraries
• Solid understanding of data pipelines, storage, and transformation
• Direct experience with dataset mitigation and filtering techniques
• Background in data quality control, validation, and automation
• Excellent communicator able to work with diverse technical teams
• Strong software engineering skills in data-intensive environments

Nice to Have

• Knowledge of machine learning principles and frameworks (e.g., PyTorch)
• Experience in front-end technologies like JavaScript
• Exposure to full-stack development or ML infrastructure
• Previous experience in high-scale software or research environments