Fullstack Software Engineer - AI Integrations (f/m/d)

Join Contentful as a Full Stack Software Engineer and innovate with AI Integrations. At Contentful, we empower thousands of customers to manage content at scale with speed, flexibility, and precision. We're looking for talented Full Stack Software Engineers who are passionate about building scalable, high-performance applications using modern technologies—and taking the lead on integrating the latest advancements in AI technology.

As a key member of our engineering team, you'll have the unique opportunity to shape the future of content operations by leveraging and integrating AI-driven solutions. You'll help create innovative features that transform how our customers manage, personalize, and deliver content across channels. From automating complex workflows to generating insights that fuel smarter decision-making, your work will drive efficiency and unlock new possibilities for businesses of all sizes.

What to expect?

• Collaborate and Innovate: Work closely with product managers, designers, and other engineers to build best-in-class AI-enabled features for our customers, ensuring seamless integration of AI technologies with existing systems.
• AI Integration: Implement and integrate AI technologies, such as generative AI, machine learning models, or NLP, to enhance our platform's capabilities, optimize processes, and improve user experiences.
• Full Stack Development: Design, develop, and maintain scalable frontend and backend features using TypeScript, React, and Node.js, while ensuring they are compatible with AI components, services, and APIs.
• Holistic Thinking: Think critically about the interactions between AI models and product components to ensure a cohesive and intuitive user experience.
• Rapid Problem-Solving: Quickly fix bugs and solve problems, particularly related to AI performance, model integration, or data pipeline issues, to enhance customer satisfaction.
• Data-Driven Development: Utilize analytics and data-driven insights to inform product decisions, and continuously improve features.
• Continuous Improvement: Participate in code reviews and contribute to improving our AI development processes, ensuring that the AI components are scalable, efficient, and maintainable.
• Stay Current: Keep up-to-date with the latest industry trends, particularly in the domains of AI and machine learning, ensuring the platform stays competitive by leveraging cutting-edge AI technologies.

What you need to be successful:

• AI and Machine Learning Experience: Demonstrable experience integrating and implementing AI/ML solutions within a product development environment. Skills in foundational models, prompt engineering and understanding to validate the quality of AI products.
• Full Stack Expertise: Strong skills in TypeScript, React, and Node.js. Deep interest in clean code practices and familiarity with enterprise architectural design patterns, particularly when working with AI-powered features.
• AI Pipeline Integration: Experience in deploying AI models to production, optimizing model performance, and integrating AI services into full stack applications.
• Containerization and Orchestration: Experience with Docker and Kubernetes, especially in deploying AI models or microservices.
• Cloud Services Familiarity: Knowledge of cloud platforms like AWS, Azure, or GCP. Experience with services such as AWS SageMaker, Google AI, or Azure Machine Learning is a plus.
• Problem-Solving Skills: A natural problem-solver who brings forward innovative ideas to integrate and features within product workflows, leading to practical solutions and product growth.
• Collaborative Mindset: Excellent communication skills and the ability to collaborate effectively in a team environment.
• Adaptability: Ability to organize and prioritize competing workloads in a fast-paced, AI-driven environment, ensuring AI and software systems align with business objectives.

Preferred:

• AI Deployment Experience: Hands-on experience with DevOps methodologies tailored for AI, including CI/CD pipelines for model deployment and MLOps practices.
• AI-Driven Automation: Previous experience working with AI automation engines, process automation using AI, or integrating AI with business logic to streamline processes.