Lead AI Engineer

Overview

In this Lead AI Engineer role, you will architect, design and deliver advanced AI solutions using state-of-the-art ML, generative and agentic AI. You’ll drive cloud-native, scalable architectures and promote responsible AI practices. You’ll lead technical delivery, mentor junior engineers, and collaborate with clients to translate business needs into trustworthy AI. This fast-paced environment rewards sound decisions and continuous learning while shaping a growing AI capability at Kainos.

Responsibilities

• Architect and deliver advanced AI solutions leveraging modern ML, generative, and agentic AI tech
• Lead adoption of modern AI frameworks, AIOps, and scalable cloud-native architectures
• Provide hands-on technical leadership and collaborate with customers to translate business challenges into AI solutions
• Ensure responsible AI practices including interpretability and ethics
• Mentor and develop junior team members and foster a culture of learning and engineering excellence
• Manage and coach a small team, focusing on performance management and career development
• Provide direction and leadership to solve challenging problems with cross-functional teams

Key requirements

• 2.1 degree in Computer Science, AI, Data Science, Statistics or related quantitative field
• Experience deploying modern AI/ML solutions into production, including prompt engineering, RAG, model evaluation, and monitoring using metrics (precision, recall, NDCG, drift)
• Strong Python with software engineering practices (CI/CD, testing, code reviews)
• Experience deploying at scale on Azure and AWS; Kubernetes and Docker for containerisation/orchestration
• Data engineering expertise for AI with large-scale, unstructured and multimodal data
• Understanding of responsible AI, model interpretability and ethics
• Strong interpersonal skills to lead client projects and elicit requirements in non-technical language
• Experience coaching and developing junior team members
• Desirable: PyTorch, TensorFlow, fine-tuning/distillation of LLMs; scikit-learn, XGBoost; vector databases; semantic search; knowledge graphs

• strong communication
• leadership and mentoring
• client-facing collaboration
• PyTorch
• TensorFlow
• scikit-learn

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