AI Engineer

We are seeking an experienced AI/ML Engineer to design, build, and scale our generative AI solutions. In this role, you will lead initiatives around large language models, image and multimodal generation, and the deployment of these technologies in production using Google Cloud Platform (GCP). You will set technical standards for GenAI, mentor the team, and ensure best practices in software engineering, collaboration, and code quality.

• Architect and develop backend services, APIs, and orchestration layers that power GenAI features, including LLM-driven workflows (RAG, agents, content generation) and image generation pipelines.
• Own end-to-end delivery of GenAI-powered features: from system design and implementation to deployment, monitoring, and iteration.
• Build and maintain scalable, production-grade infrastructure across GCP and Azure, leveraging services such as Vertex AI, Cloud Run, Azure OpenAI Service, Azure Web Apps, and Azure Application Insights.
• Design and maintain CI/CD pipelines for GenAI applications, ensuring reliable deployment, versioning, and rollback of model integrations and service dependencies.
• Enforce software engineering best practices across the codebase: modularity, test coverage, code reviews, documentation, and observability.
• Champion strong Git workflows such as branching strategies, pull request hygiene, and issue tracking to keep a growing team shipping cleanly and collaboratively.
• Evaluate and integrate third-party AI providers and models (OpenAI, Google, etc.) as managed dependencies, applying appropriate abstraction and vendor isolation.

• Mentor engineers on the team, lead technical discussions, and drive knowledge sharing across the engineering organisation.

• Master's degree in Computer Science, Software Engineering, or a related field.

• 3–6 years of professional software engineering experience, with at least 1–2 years working on systems that integrate or productionise AI/ML components.

• Strong backend engineering skills in Python; experience designing APIs, async systems, and service-oriented architectures.

• Solid experience deploying and operating workloads across both GCP (Cloud Run, Vertex AI, Pub/Sub, BigQuery, etc.) and Microsoft Azure environments.

• Proven command of software engineering fundamentals: CI/CD, containerisation (Docker/Kubernetes), testing strategies, logging, and monitoring.

• Practical experience integrating LLM APIs (OpenAI, Gemini, Claude, HuggingFace, etc.) and image generation services into production applications such as model selection, prompt management, and output handling included.

• Strong Git and collaboration practices: branching, merging, code review, and PR management in a team environment.

• Awareness of prompt safety, output validation, and responsible AI considerations in production systems.

• Clear communicator and collaborator; comfortable working across engineering, product, and design in a multidisciplinary team.