Senior AI Engineer

• Design, build, evaluate and operate production AI systems solving product and engineering problems
• Own complex AI systems end-to-end, from ambiguous business problem discovery through experimentation, implementation, deployment, monitoring and continuous improvement
• Work on AI-native product engineering and knowledge systems
• Develop automated and reusable vendor-integration capabilities and evolve the Integration Hub
• Enable AI-assisted software delivery across analysis, requirements, implementation, testing, knowledge reuse, code review and delivery acceleration
• Build AI-enabled quality and production intelligence capabilities, including anomaly detection, failure-pattern analysis, data-quality/error detection and intelligent monitoring
• Select AI approaches based on evidence, including prompting, retrieval, orchestration, predictive ML, model adaptation/fine-tuning and specialized models
• Build production systems using model APIs, structured outputs, retrieval/RAG, agentic and tool-based workflows and predictive techniques
• Design evaluations covering functional behaviour, groundedness, business correctness, robustness and regressions
• Establish monitoring and feedback loops using evaluation evidence, logs, metrics, traces and operational data
• Engineer for reliability, testability, observability, maintainability, latency and cost
• 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
• Collaborate with Architecture, Platform, Security, Data, Quality, Product and domain specialists while retaining technical accountability
• Mentor less-experienced engineers and contribute reusable patterns, knowledge and practices

• Strong hands-on experience delivering production AI systems
• Experience with model APIs, structured outputs, tool-based systems, retrieval/RAG, agentic workflows, predictive techniques and model adaptation/fine-tuning where appropriate
• Ability to select the appropriate AI approach based on problem context and evaluation evidence
• Experience designing behavioural, groundedness/business-correctness, robustness and regression evaluations, including production evaluation, monitoring and feedback loops
• Strong software-engineering background, particularly Python
• Experience with distributed systems, APIs, integrations, data pipelines, automated testing, CI/CD, containers/cloud and production-grade engineering practices
• Strong observability and production troubleshooting skills, including logs, metrics and traces
• Reliability, testability, latency/cost awareness and complex failure investigation skills
• Ability to understand complex workflows, business rules, dependencies and constraints and translate ambiguous business problems into reliable system behaviour
• Demonstrated ability to take complex technical problems from discovery and experimentation through production and continuous improvement
• Fluent English, written and spoken
• Experience with MCP or equivalent tool/context interoperability patterns (desirable)
• Experience with orchestration technologies such as LangGraph, Temporal or equivalent (desirable)
• Experience with model adaptation/fine-tuning or production predictive ML (desirable)
• Experience building evaluation harnesses and datasets from real production/user feedback (desirable)
• Experience with unstructured data and knowledge/retrieval systems at scale (desirable)
• Working knowledge of AI security, privacy and responsible-AI practices (desirable)
• Experience improving developer/product workflows through applied AI (desirable)
• Experience with anomaly detection, intelligent monitoring or AI-assisted operational analysis (desirable)

• Work-from-anywhere scheme
• 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