Who we are
We 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 doing
- Analyze 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 practices
- Develop robust backend services, APIs, asynchronous processes, and integration components using an appropriate production-grade technology stack
- Integrate machine-learning and generative-AI services through model APIs, SDKs, internally hosted endpoints, or platform services, with clear abstraction and versioning
- Implement applied AI patterns such as prompt and template management, structured outputs, retrieval-augmented generation, embeddings, vector search, tool calling, and workflow orchestration where appropriate
- Build secure data ingestion, transformation, retrieval, and persistence flows that preserve data quality, provenance, access controls, and privacy requirements
- Create automated unit, integration, contract, end-to-end, and regression tests, together with AI-specific evaluations for quality, groundedness, safety, latency, and cost
- Implement input and output validation, content safeguards, fallback behavior, human-in-the-loop controls, and graceful degradation for uncertain or unavailable AI responses
- Instrument solutions with meaningful logs, metrics, traces, usage indicators, quality signals, model or prompt version data, and operational alerts
- Contribute 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 misuse
- Diagnose production incidents across the full application and AI integration chain, perform root-cause analysis, and implement sustainable corrective actions
- Document architectures, APIs, data flows, prompts, model dependencies, evaluations, operational procedures, limitations, and technical decisions
- Participate in code and design reviews, mentor less experienced engineers, and contribute to reusable patterns and engineering standards
What you'll bring along
- BSc/MSc in Computer Science or related field
- 8+ years of software engineering experience
- Strong full-stack software-engineering experience with at least one modern frontend framework and one production backend stack
- Solid knowledge of JavaScript or TypeScript and familiarity with technologies such as Angular, React, Node.js, Python, Java, Spring Boot, or equivalent frameworks
- Experience working with AI assistant platforms and CLI tools
- Hands-on experience integrating machine-learning or generative-AI capabilities into user-facing or enterprise software solutions
- Demonstrable ability to take an AI-enabled feature from technical design and implementation through testing, deployment, monitoring, and support
- Strong commitment to software quality, security, maintainability, documentation, and measurable user or business outcomes
- Proactive, experimental, and evidence-driven mindset, with the discipline to validate AI behavior rather than rely on demonstrations alone
- Experience 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 orchestration
- Experience with prompt engineering, structured outputs, tool or function calling, model selection, context management, and AI response evaluation
- Knowledge of SQL and NoSQL data stores, search technologies, caching, data pipelines, and secure access to enterprise information
- Experience with automated testing, Git, CI/CD, containerization, cloud services, observability, and production-support practices
- Working knowledge of responsible-AI, privacy, security, explainability, human oversight, and risk-control principles for AI-enabled products
- Ability to troubleshoot across browser, API, application, data, model, and infrastructure layers
- Strong communication, documentation, analytical, collaboration, and product-oriented problem-solving skills
- Professional working proficiency in English