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About Infiterra
Join 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 Role
As 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 On
AI-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-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 Like
In 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 Are
You 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.
Responsibilities
Own 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, model choice, adaptation/fine-tuning or other techniques.
Diagnose complex AI and software failures and convert production learning into prevention and system improvement.
Identify and validate AI-enabled improvements to SDLC, Product and operational workflows.
Explore AI-enabled platform-quality capabilities such as anomaly detection, data-quality/error detection, failure-pattern analysis and intelligent monitoring where they provide measurable value.
Work with Architecture, Platform, Security, Data, Quality, Product and domain specialists while retaining technical accountability for the AI capability within your scope.
Mentor less-experienced engineers and contribute reusable patterns, knowledge and practices.
Qualifications Essential
AI systems: Strong hands-on experience delivering production AI systems; model APIs, structured outputs, tool-based systems, retrieval/RAG, agentic workflows, predictive techniques and model adaptation/fine-tuning where appropriate; able to select the appropriate AI approach based on problem context and evaluation evidence.
Evaluation: Experience designing behavioural, groundedness/business-correctness, robustness and regression evaluations, including production evaluation, monitoring and feedback loops.
Engineering: Strong software-engineering background, particularly Python; distributed systems, APIs, integrations, data pipelines, automated testing, CI/CD, containers/cloud and production-grade engineering practices.
Production: Strong observability and production troubleshooting skills including logs, metrics and traces; reliability, testability, latency/cost awareness and complex failure investigation.
Business/domain: Ability to understand complex workflows, business rules, dependencies and constraints and translate ambiguous business problems into reliable system behaviour.
Ownership: Demonstrated ability to take complex technical problems from discovery and experimentation through production and continuous improvement.
Fluent English, written and spoken.
Desirable Experience with MCP or equivalent tool/context interoperability patterns.
Experience with orchestration technologies such as LangGraph, Temporal or equivalent.
Experience with model adaptation/fine-tuning or production predictive ML.
Experience building evaluation harnesses and datasets from real production/user feedback.
Experience with unstructured data and knowledge/retrieval systems at scale.
Working knowledge of AI security, privacy and responsible-AI practices.
Experience improving developer/product workflows through applied AI.
Experience with anomaly detection, intelligent monitoring or AI-assisted operational analysis.
Benefits
Work-from-anywhere scheme — plus regular team get-togethers in person, with travel and accommodation covered.
Flexible working hours.
Health and life insurance programme.
Learning & development budget.
Tech-driven, friendly team with an international mindset.
If you feel you’re a great fit, please apply! We’d love to hear from you!
All applications will be treated with confidentiality. Please note that due to the high volume of CVs received, only candidates who are a good fit will be contacted for an interview.
As part of our commitment to diversity in the workforce, Infiterra is dedicated to Equal Employment Opportunity, ensuring that all individuals are treated with respect and consideration without regard to race, color, national origin, ethnicity, gender, disability, sexual orientation, gender identity, or religion.