Software engineer, generative AI (UK)

📋 Description

• • Design and develop robust, scalable, and secure generative AI services and applications using Python and modern frameworks to drive enterprise-wide transformation
• • Build and optimize high-performance, low-latency APIs and microservices for integrating advanced AI models and agentic workflows into the platform
• • Collaborate closely with product managers, data scientists, and cross-functional engineering teams to translate complex business needs into innovative AI solutions from concept to production
• • Implement and maintain responsive user interfaces (primarily focused on backend enablement though some frontend interaction is expected) using React and TypeScript to deliver intuitive user experiences
• • Partner with DevOps teams to build continuous deployment, logging, and monitoring systems, ensuring top-tier performance and reliability
• • Own key architectural components, ensuring best practices in code quality, security, and maintainability through rigorous testing and peer reviews
• • Leverage large language models (LLMs), vector databases (e.g., Pinecone, Weaviate, pgvector), and modern open-source agentic frameworks to power enterprise-grade AI applications
• • Develop and deploy AI-driven solutions that are grounded in company-specific data and integrated into WRITER’s end-to-end platform used by enterprises like Mars, Marriott, Uber, and Vanguard
• • Work in a hybrid environment based out of the London hub, contributing to a product that redefines enterprise productivity through AI-powered automation
• • Ensure secure, scalable, and maintainable codebases by adhering to industry-standard practices in testing, code reviews, and system architecture
• • Contribute to the evolution of the platform by proactively identifying and implementing innovative improvements to systems and processes
• • Mentor junior engineers and actively engage in collaborative settings to achieve shared team goals
• • Translate complex technical requirements into functional AI systems that meet enterprise security, compliance, and performance standards
• • Utilize cloud platforms such as AWS, GCP, or Azure, along with containerization tools like Docker and Kubernetes, to deploy and manage AI services
• • Work with database systems including PostgreSQL and modern web technologies such as FastAPI and Asyncio to deliver efficient, high-throughput backend services
• • Maintain a strong focus on performance, reliability, and user experience while scaling AI capabilities across diverse enterprise use cases
• • Participate in end-to-end development cycles from ideation through production deployment, ensuring seamless integration of AI models with existing enterprise workflows
• • Uphold a culture of accountability by taking full ownership of code quality and system resilience from initial design to ongoing maintenance