Senior Software Engineer (AI, AWS & Python)
- Design, develop, deploy, monitor, scale, and troubleshoot ML and LLM-based systems that power shared AI services across the platform
- Build secure, high performance microservices and platform components on AWS, owning services end-to-end from implementation through deployment and production monitoring
- Collaborate with Applied Scientists, Product teams, and other Software Engineers to implement and productize AI-based capabilities
- Evaluate and pilot new tools including latest AI tooling to improve performance, cost efficiency, and developer productivity
- Shape and evolve AI platform infrastructure by applying established organisational patterns and improving them where needed
- Design and develop reusable AI libraries, frameworks, and internal tooling that accelerate delivery across multiple teams
- Stay ahead of AI and platform trends, adapting designs and practices so systems remain robust, compliant, and future‑proof
- 5+ years of experience delivering secure, scalable applications in agile environments
- Strong ability to write highly readable, maintainable, and well-tested code with a solid grounding in software design principles
- Strong hands-on skills in Python and Node.js/TypeScript on a major cloud platform preferably AWS including serverless or microservice patterns, infrastructure as code (CDK/Terraform), CI/CD, containerization, and observability tooling
- Experience building and operating backend services or microservices in a major cloud environment preferably AWS
- Good understanding of the full lifecycle of AI-based solutions from experimentation and implementation through deployment, monitoring, and iteration
- Practical experience working with LLM-based systems or adjacent AI technologies
- Ability to collaborate effectively across disciplines and help translate applied science work into robust product capabilities
- Habit of integrating security best practices including IAM, secrets management, privacy controls into everything built including AI components
- Excellent communication and leadership skills to explain complex technical and AI topics clearly to different audiences
- Hands-on experience with AWS CDK
- Hands-on experience with LLM-related techniques such as prompt engineering, fine-tuning, model evaluation, RAG, agentic solutions, or MCP servers
- Experience developing AI evaluation, observability, or governance mechanisms for production systems
- Experience shaping shared platform capabilities or reusable engineering patterns across multiple teams
- Experience improving performance, reliability, or cost efficiency for cloud-based AI or data-intensive services
- Interest or experience in mentoring peers, raising engineering standards, and helping teams adopt better ways of working
- Flexible work environment
- Global days of service
- Comprehensive health benefits
- Meeting free days
- Generous time off policy
- Wellness programs
- Hybrid work model with onsite work at least 50% of the time if within commuting distance to an office location