Senior AI Engineer

About InfiterraJoin our mission to grow and transform the subscription economy by simplifying subscription service delivery.Infiterra enables IT distributors, Managed Service Providers (MSPs), and telcos to succeed in the subscription economy. Our subscription commerce platform automates and unifies subscription workflows - from quote to bill- driving operational efficiency, billing accuracy, and scalable growth.Recognized as a global leader in subscription commerce, Infiterra combines innovation, performance excellence, and trusted expertise to help partners transform and grow.About The RoleAs a Senior AI Engineer, you will be part of R&D and will design, build, evaluate and operate production AI systems that solve meaningful product and engineering problems.This is an Applied AI Engineering role combining deep AI specialization, strong software engineering and business/domain understanding. You will work across LLM-based systems, retrieval, agents, tool use, predictive techniques and model adaptation/fine-tuning where appropriate - choosing the approach based on the problem and evidence rather than defaulting to one AI technique.You will take complex AI capabilities end-to-end: from understanding an ambiguous business problem, through experimentation and solution design, to implementation, evaluation, deployment, monitoring and continuous improvement.This is a fully remote senior individual-contributor role based in either Greece or the UK, with periodic in-person collaboration.Our AI engineering environment includes Python, cloud-native services, containers/Kubernetes, CI/CD, model APIs, retrieval and agent/tool frameworks, and evaluation/observability tooling. We choose technologies based on the problem rather than standardizing on one AI framework.What You Will Work OnAI-native product engineering and knowledge systems that reconcile business knowledge from product, engineering and production evidence.Automated and reusable vendor-integration capabilities, including the evolution of our Integration Hub from implementation automation toward learning from real integration outcomes.AI-assisted software delivery: analysis, requirements, implementation, testing, knowledge reuse, code review and delivery acceleration. AI-enabled quality and production intelligence, such as anomaly detection, failure-pattern analysis, data-quality/error detection and intelligent monitoring. Predictive, analytical or model-adaptation capabilities where evidence shows they are the right solution. What Success Looks LikeIn your first 90 days, you will have developed a strong understanding of our platform and domain, contributed materially to a production AI capability, established meaningful evaluation evidence around your work, and identified opportunities where AI can create measurable product or engineering leverage.By six months, you will independently own one or more complex production AI systems or workflows - evaluated, observable and operationally reliable - and will have introduced reusable technical knowledge or practices that improve how the wider engineering team builds with AI.Success is not measured by the number of AI features introduced. It is measured by business correctness, AI behavioural quality, engineering quality, production reliability and demonstrated value.Who You AreYou are comfortable being given a business problem rather than a detailed technical specification. You can experiment quickly, but you also do the harder engineering work required to make AI behaviour measurable, reliable and maintainable in production. You understand that AI output is not automatically truth, make assumptions and limitations explicit, and design evaluation and human controls according to risk. You are equally comfortable discussing model behaviour, APIs and distributed-system failures as you are understanding the business rules the system is expected to support.ResponsibilitiesOwn the design, implementation, evaluation and production operation of complex AI systems involving multiple components, integrations or workflows.Translate ambiguous business problems, rules and domain constraints into measurable AI/system behaviour.Select the appropriate AI approach based on evidence - including prompting, retrieval, orchestration, predictive ML, model adaptation/fine-tuning or specialized models where justified.Build production systems using model APIs, structured outputs, retrieval/RAG, agentic and tool-based workflows, predictive techniques and other appropriate AI patterns.Design evaluation strategies covering functional behaviour, groundedness, business correctness, robustness and regressions.Establish production monitoring and feedback loops using evaluation evidence, logs, metrics, traces and operational data.Engineer for reliability, testability, observability, maintainability, latency and cost.Design production learning loops and determine whether behaviour should be improved through context, retrieval, orchestration, mode