Role Description As a Lead AI Engineer at Kainos, you will architect, design, and deliver advanced AI solutions leveraging state-of-the-art machine learning, generative and agentic AI technologies. Your responsibilities include: - Driving the adoption of modern AI frameworks, AI Ops best practices, and scalable cloud-native architectures. - Providing hands-on technical leadership and collaborating with customers to translate business challenges into trustworthy AI solutions. - Ensuring responsible AI practices throughout the development process. - Fostering a culture of innovation, continuous learning, and engineering excellence as a technical mentor. - Managing, coaching, and developing a small number of staff, focusing on employee performance and career development. - Providing direction and leadership for your team to solve challenging problems together. Qualifications - A minimum of a 2.1 degree in Computer Science, AI, Data Science, Statistics, or a similar quantitative field. - Demonstrable experience of deploying modern AI/ML solutions into production, including prompt engineering, retrieval-augmented generation (RAG), model evaluation, and monitoring using metrics (e.g., precision, recall, NDCG, and drift detection). - Strong Python skills with a grounding in software engineering best practices (CI/CD, testing, code reviews, etc.). - Experience developing solutions at scale on cloud platforms (Azure & AWS), containerization, and orchestration tools such as Kubernetes and Docker. - Expertise in data engineering for AI: handling large-scale, unstructured, and multimodal data. - Understanding of responsible AI principles, model interpretability, and ethical considerations. - Strong interpersonal skills with the ability to lead client projects and establish requirements in non-technical language. - Experience in managing, coaching, and developing junior members of a team and wider community. Requirements - Demonstrable experience with modern deep learning frameworks (e.g., PyTorch, TensorFlow), fine-tuning or distillation of LLMs (e.g., GPT, Llama, Claude, Gemini), machine learning libraries (e.g., scikit-learn, XGBoost). - Experience with data storage for AI, vector databases, semantic search, and knowledge graphs. - Contributions to open-source AI projects or research publications. - Familiarity with AI security, privacy, and compliance standards (e.g., ISO42001). Benefits - People-first culture where your ideas are valued and growth is supported. - Opportunity to be part of a diverse, ambitious team that celebrates creativity and collaboration.

Role Description As an AI Engineer Manager at Kainos, you’ll be responsible for successful delivery of advanced AI solutions leveraging state-of-the-art machine learning, generative and agentic AI technologies. You will drive the adoption of modern AI frameworks, AIOps best practices, and scalable cloud-native architectures. Your role will involve technical leadership, engaging with senior stakeholders to agree architectural principles, strategic direction, and system architecture. - Foster a culture of innovation, continuous learning, and engineering excellence. - Manage, coach, and develop a team with a focus on development of standards and policies. - Build enduring customer relationships and embed commercial acumen. - Provide direction and leadership for your team as you solve challenging problems together. Qualifications - A minimum of a 2.1 degree in Computer Science, AI, Data Science, Statistics, or in a similar quantitative field. - Proven experience of leading multi-disciplinary teams to deliver AI/ML solutions to production. - Demonstrable experience of technical leadership for AI delivery including architecture, product design principles, and engineering excellence. - Strong Python skills with a grounding in software engineering best practices (CI/CD, testing, code reviews, etc.). - Experience developing solutions at scale on cloud platforms (Azure & AWS), containerisation, and orchestration tools such as Kubernetes and Docker. - Expertise in data engineering for AI: handling large-scale, unstructured, and multimodal data. - Understanding of responsible AI principles, model interpretability, and ethical considerations. - Strong interpersonal skills with the ability to lead client projects, manage C-level stakeholders, and establish requirements/architecture concepts. - Experience in managing, coaching, and developing junior members of a team and wider community. Requirements - Experience with the latest AI engineering approaches such as prompt engineering, retrieval-augmented generation (RAG), agentic AI, model evaluation, and monitoring using metrics (e.g. precision, recall, NDCG, and drift detection). Desirable - Demonstrable experience with modern deep learning frameworks (e.g. PyTorch, TensorFlow), fine-tuning or distillation of LLMs (e.g. GPT, Llama, Claude, Gemini), machine learning libraries (e.g. scikit-learn, XGBoost). - Experience with data storage for AI, vector databases, semantic search, and knowledge graphs. - Actively contributes to open-source AI projects, research publications, and industry events/websites. - Familiarity with AI security, privacy, and compliance standards e.g. ISO42001. - Experience of shaping commercial AI offerings, winning AI work, and wider pre-sales support. Benefits - We believe in the power of diversity, equity, and inclusion. - We are committed to building a team that is as diverse as the world we live in. - We actively seek out talented people from all backgrounds. - Our friendly talent acquisition team is here to support you every step of the way. - We encourage candidates to reach out for accommodations or adjustments during the recruitment process.

• Define, design, and supervise the technical architecture of solutions based on intelligent agents and LLMs, integrating tools such as LangChain, LlamaIndex, AutoGen, CrewAI, or equivalent frameworks. • Implement MCP (Model Context Protocol) and A2A (Agent-to-Agent) architectures to enable multi-agent coordination and autonomous flows within business environments. • Work with the MLOps team and execution environments that enable continuous agent updating and deployment, including memory management, context, and long-term planning. • Collaborate closely with product, UX, data, and backend teams to map business needs to intelligent agent architectures.

• Implement and maintain open source AI integrations, frameworks and example applications using languages commonly used in the open source ecosystem, including Python, TypeScript, Go, and Rust. • Build and maintain public sample applications, SDK extensions, notebooks, and reference implementations demonstrating AI and cloud storage best practices. • Use AI-assisted development tools, including Claude Code, to accelerate implementation while validating correctness, security, and performance. • Contribute to open source projects through code contributions, issue triage, reviews, and ongoing maintenance. • Ensure all open source assets meet high standards for testing, documentation, reproducibility, and developer usability. • Maintain clean, well-structured GitHub repositories intended for external adoption and community contribution. • Collaborate with Product and QA to ensure integrations reflect real customer AI workflows and Backblaze B2 usage patterns.