Who We AreWe are looking for Senior Full-Stack AI Engineers to design, build, integrate, and operate end-to-end digital capabilities that combine modern web applications with artificial intelligence services. The role will cover user experience, backend APIs, AI orchestration, data access, evaluation, observability, security, deployment, and production support.The engineers will collaborate with Product, Architecture, Data and AI, Cybersecurity, Quality Assurance, DevOps, and business teams to convert prioritized use cases into reliable and maintainable solutions. They must be pragmatic software engineers who can determine where AI adds measurable value, integrate external or internally hosted models, and implement appropriate guardrails, human oversight, and quality controls.What You'll Be DoingAnalyze business needs, user journeys, data constraints, risks, and non-functional requirements for AI-enabled application capabilities Design end-to-end solution components covering frontend experience, backend services, AI orchestration, data retrieval, integrations, security, and operational support.Develop accessible, responsive, and maintainable user interfaces using a modern frontend framework and sound component, state-management, and testing practicesDevelop robust backend services, APIs, asynchronous processes, and integration components using an appropriate production-grade technology stackIntegrate machine-learning and generative-AI services through model APIs, SDKs, internally hosted endpoints, or platform services, with clear abstraction and versioningImplement applied AI patterns such as prompt and template management, structured outputs, retrieval-augmented generation, embeddings, vector search, tool calling, and workflow orchestration where appropriateBuild secure data ingestion, transformation, retrieval, and persistence flows that preserve data quality, provenance, access controls, and privacy requirementsCreate automated unit, integration, contract, end-to-end, and regression tests, together with AI-specific evaluations for quality, groundedness, safety, latency, and costImplement input and output validation, content safeguards, fallback behavior, human-in-the-loop controls, and graceful degradation for uncertain or unavailable AI responsesInstrument solutions with meaningful logs, metrics, traces, usage indicators, quality signals, model or prompt version data, and operational alertsContribute to CI/CD, containerization, environment configuration, release automation, production verification, rollback, and controlled model or prompt promotion Apply secure-development practices covering authentication, authorization, secrets, dependency management, data protection, API security, and protection against AI-specific misuseDiagnose production incidents across the full application and AI integration chain, perform root-cause analysis, and implement sustainable corrective actionsDocument architectures, APIs, data flows, prompts, model dependencies, evaluations, operational procedures, limitations, and technical decisionsParticipate in code and design reviews, mentor less experienced engineers, and contribute to reusable patterns and engineering standardsWhat You'll Bring AlongBSc/MSc in Computer Science or related field8+ years of software engineering experienceStrong full-stack software-engineering experience with at least one modern frontend framework and one production backend stackSolid knowledge of JavaScript or TypeScript and familiarity with technologies such as Angular, React, Node.js, Python, Java, Spring Boot, or equivalent frameworksExperience working with AI assistant platforms and CLI toolsHands-on experience integrating machine-learning or generative-AI capabilities into user-facing or enterprise software solutionsDemonstrable ability to take an AI-enabled feature from technical design and implementation through testing, deployment, monitoring, and supportStrong commitment to software quality, security, maintainability, documentation, and measurable user or business outcomesProactive, experimental, and evidence-driven mindset, with the discipline to validate AI behavior rather than rely on demonstrations aloneExperience designing REST or event-driven integrations, data models, authentication flows, asynchronous processing, and resilient distributed applications Practical understanding of machine-learning and generative-AI concepts, model APIs, embeddings, retrieval-augmented generation, vector stores, and AI workflow orchestrationExperience with prompt engineering, structured outputs, tool or function calling, model selection, context management, and AI response evaluationKnowledge of SQL and NoSQL data stores, search technologies, caching, data pipelines, and secure access to enterprise informationExperience with automated testing, Git, CI/CD, containerization, cloud services, observability, and production-support practicesWorking knowledge of responsible-AI, privacy, security, explainability, human oversight, and risk-control principles for AI-enabled productsAbility to troubleshoot across browser, API, application, data, model, and infrastructure layersStrong communication, documentation, analytical, collaboration, and product-oriented problem-solving skillsProfessional working proficiency in English Show more Show less