Senior AI Software Engineer – Causal AI

London - Hybrid

About the Role

We are seeking a highly skilled Senior AI Software Engineer to lead the development and deployment of advanced Causal AI solutions. The ideal candidate will bring strong expertise in Python engineering, applied machine learning, and causal inference methods, coupled with excellent communication skills to collaborate with diverse teams and stakeholders. This role is hands-on, combining research-driven innovation with practical engineering to deliver scalable, production-ready AI systems.

Key Responsibilities

• Design, implement, and optimise Causal AI algorithms and frameworks for real-world applications.
• Develop and maintain Python-based AI pipelines, ensuring efficiency, scalability, and reliability.
• Build and deploy multi-agent AI systems, integrating methods such as causal discovery, uplift modeling, and algorithmic fairness.
• Collaborate with cross-functional teams to understand customer and business requirements, translating them into technical solutions.
• Lead CI/CD processes and contribute to engineering best practices, ensuring high-quality code and reproducibility.
• Contribute thought leadership by writing technical blogs, presenting at conferences, and representing the company at industry meetups.
• Mentor junior engineers and foster a culture of continuous learning and innovation within the AI team.

Required Qualifications & Skills

• Strong programming expertise in Python, with experience building production-level ML/AI systems.
• Advanced knowledge of Causal AI / Causal Inference, including methods like algorithmic recourse, causal impact analysis, and uplift modeling.
• Proven experience in machine learning, deep learning, or NLP at scale.
• Hands-on experience with CI/CD pipelines, GitHub workflows, and collaborative coding environments.
• Excellent communication skills, with a track record of presenting complex AI concepts clearly to technical and non-technical audiences.

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  Preferred Qualifications

  • Experience with Generative AI techniques (e.g., Retrieval Augmented Generation, multi-agent systems, function calling).
  • Experience with large-scale datasets in healthcare, finance, or e-commerce.
  • Contributions to open-source AI/ML projects or published research in top-tier journals/conferences.