FullStack Engineer - AI Platforms (f/m/d)

Contentful
London
11 months ago
Applications closed

Related Jobs

View all jobs

Full-Stack Data Engineer (Security Cleared)

Full-Stack Data Engineer (Security Cleared)

Senior Full Stack Data Engineer (Client Facing)

Applied AI Engineer: Full-Stack, Cloud & NLP Impact

Full-Stack Data Analyst: SQL, Dashboards & Insights

Advisory Data Engineer

About the Opportunity

Join Contentful as an AI 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 are seeking talented Senior AI Software Engineers (f/m/d) to join our AI Platform team—a group dedicated to building and maintaining an internal AI platform that accelerates innovation across our organization.

As a key contributor, you will help shape the internal AI ecosystem by developing scalable, high-performance AI integrations, APIs, tools and documentation that empower feature teams to efficiently test, deliver and maintain cutting-edge AI-driven products. Acting as both a support and consultancy resource, you’ll collaborate closely with feature teams to enhance their development capabilities and bring AI-powered solutions to life. By building intuitive internal tooling and fostering seamless integration of AI technologies, your work will drive efficiency and innovation throughout Contentful.

If you are passionate about advancing AI technology, enabling teams with powerful tools, and redefining how we build and deliver AI solutions, we’d love to have you join us. Together, we’ll build systems that set new standards for AI integration and empower our teams to create smarter, more impactful content solutions.

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.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.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.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.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

Full Stack Development: Proficiency in TypeScript, React, and Node.js with a solid understanding of clean code practices and exposure to enterprise architectural design patterns.API and Service Design: Demonstrated experience in developing APIs and services capable of handling moderate traffic efficiently.Asynchronous Systems: Experience in building and maintaining asynchronous systems to support automation and bulk data processing.Containerization and Orchestration: Hands-on experience with Docker and Kubernetes, particularly in deploying microservices or simple AI models.Cloud Services Exposure: Familiarity with cloud platforms like AWS, Azure, or GCP, and experience with services like AWS Lambda, Google Cloud Functions, or Azure App Services is a plus.Problem-Solving Approach: A proactive problem-solver who contributes practical ideas for improving workflows and enabling product enhancements.Team Collaboration: Strong communication skills and a proven ability to work effectively within a team.Adaptability: Capable of prioritizing tasks and managing workloads in dynamic environments, aligning systems with evolving business needs.

Preferred:

AI and Machine Learning Expertise:Skills in 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.Platform Experience: Experience in developing platform features to enable internal teams.

What's in it for You?

Join an ambitious tech company reshaping the way people build digital experiences Full-time employees receive Stock Options for the opportunity to share in the success of our company Comprehensive healthcare package covering 100% of monthly health premiums for employees and 85% of costs for your dependents. Fertility and family building benefits, including a lifetime reimbursable wallet to support your growing family. We value Work-Life balance and You Time! A generous amount of paid time off, including vacation days, sick days, compassion days for loss, education days, and volunteer days Company paid parental leave to care for and focus on your growing family Use your personal annual education budget to improve your skills and grow in your career Enjoy a full range of virtual and in-person events, including workshops, guest speakers, and fun team activities, supporting learning and networking exchange beyond the usual work duties An annual wellbeing stipend to care for your physical, financial, or emotional health A monthly communication stipend and phone hardware upgrade reimbursement. New hire office equipment stipend for hybrid or distributed employees. Get the gear you need to work at your best.

#LI-SB1 #LI-Hybrid

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.

The Skills Gap in Machine Learning Jobs: What Universities Aren’t Teaching

Machine learning has moved from academic research into the core of modern business. From recommendation engines and fraud detection to medical imaging, autonomous systems and language models, machine learning now underpins many of the UK’s most critical technologies. Universities have responded quickly. Machine learning modules are now standard in computer science degrees, specialist MSc programmes have proliferated, and online courses promise to fast-track careers in the field. And yet, despite this growth in education, UK employers consistently report the same problem: Many candidates with machine learning qualifications are not job-ready. Roles remain open for months. Interview processes filter out large numbers of applicants. Graduates with strong theoretical knowledge struggle when faced with practical tasks. The issue is not intelligence or effort. It is a persistent skills gap between university-level machine learning education and real-world machine learning jobs. This article explores that gap in depth: what universities teach well, what they routinely miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in machine learning.