Machine Learning/ AI Engineer

Triumph Research Intelligence is a SaaS business on a mission to make Risk-Based Quality Management (RBQM) simple for all clinical trials. Our platform, OPRA, leads to safer patients and happier clinical trials teams – with the end goal being higher quality trials, better drugs getting to market quicker , and ultimately, more lives saved. What’s more important than that? 

As the company rapidly scales, accelerated by its strong product-market fit, we need to increase our security, so the time is right for an experienced Machine Learning/AI Engineer to join our newly formed Data Innovations Team.

• Evangelise the enablement of AI across the organisation — advocating for the adoption of AI technologies and ensuring solutions are robust, ethical, secure, and aligned with business strategy.
• Collaborate closely with the CSO, Data Science and other teams to convert business problems into AI solutions, delivering high-value prototypes and production-grade models.
• Develop, train, evaluate, and optimise ML and deep learning models using modern frameworks.
• Build and validate generative AI solutions, including LLM applications, RAG pipelines, and tuned foundation models.
• Implement data pipelines, feature engineering, and model evaluation workflows using best-practice MLOps.
• Deploy, monitor, and maintain ML models in production, ensuring reliability and performance tracking.
• Ensure all AI/ML solutions meet quality, explainability, compliance, and performance requirements.
• Provide thought leadership on AI/ML strategy, governance, and delivery best practices.
• Ensure alignment with security, ethical AI, privacy, and regulatory requirements.
• Create and maintain documentation on AI and Machine Learning processes and systems.

• Degree in computer science, engineering, ML, or equivalent related experience.
• Strong experience building ML and deep learning models using Python.
• Experience developing LLM-based applications and RAG architecture.
• Strong software engineering skills including Git, CI/CD, Docker, and API development.
• Understanding of MLOps tools such as MLflow, Azure ML, or similar platforms.
• Familiarity with responsible AI principles: explainability, fairness, privacy, and regulatory compliance.
• Excellent communication skills with the ability to explain AI concepts clearly to non-technical stakeholders.
• Proven track record of delivering AI/ML solutions in a production environment.