• Architect & Build AI Features: Design and implement robust classical ML and generative AI solutions, striking the right balance between autonomous agentic architectures and deterministic pipelines. • Evaluate: Design and maintain evaluation frameworks to measure AI quality, reliability, safety, and business impact before and after deployment. • Integrate & Deploy: Partner closely with full-stack developers and DevOps to seamlessly integrate AI capabilities into client web and mobile applications using serverless architecture (e.g., AWS Lambda) or API endpoints. • Optimize for Production: Refine prompts, system instructions, and chunking strategies to balance accuracy, latency, token consumption, and data privacy. • Traditional Predictive Analytics: Clean and process unstructured or historical client data to train/fine-tune custom algorithms for specific business problems (such as forecasting, classification, or anomaly detection). • Collaborate & Communicate: Actively participate in client discovery sessions, translate ambiguous business requirements into viable technical scopes, and demo prototypes directly to stakeholder teams. • Maintain Engineering Excellence: Engage in constructive code reviews, implement rigorous validation patterns to test AI outputs, and contribute templates or runbooks to our internal AI knowledge base.

Role Description Kestra orchestrates workflows across systems and domains, and AI agents and LLM-driven workflows are a growing part of that. This is a builder role. We want someone who can take an AI use case and build it end to end, from the Kestra engine through to the UI, and get it in front of users fast. Prototype use cases and turn the best ones into features and blueprints. If you are an unusually strong full-stack builder with real AI experience, we want to talk to you even if you do not fit a standard job box. - Build AI features end to end, backend and frontend - Prototype agent use cases quickly - Build the product surface (UI, blueprints, examples) that makes AI workflows usable in Kestra - Work closely with product and the backend team, with a short path from idea to released feature Qualifications - Strong full-stack engineering with Java on the backend - Frontend experience is a clear plus - Hands-on AI experience: you have built with AI and understand AI agents - A bias toward building and releasing, balanced with the judgment to know when a prototype is ready for production Requirements - Backend: Java, Micronaut - Frontend: Vue.js, Bootstrap - Datastore: Kafka, Elasticsearch, PostgreSQL, MySQL - Infrastructure: Docker, Kubernetes, Terraform - Cloud: GCP, AWS, Azure - Tools: GitHub for repo management, actions, and issues Benefits - Work from anywhere: We’re a remote-first company, so you can work from wherever feels like home. Plus, you’ll have access to coworking spaces worldwide if you ever need a change of scenery. - Health coverage: From medical support, dental, and vision, we've got you covered. - Home office setup on us: We’ll provide all the equipment you need to work comfortably. Company Description In March 2026, we closed a $25M Series A led by RTP Global, with participation from Alven, ISAI, and Axeleo – backed by founders from Datadog, dbt Labs, and Hugging Face.

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.