Requirements

3+ years of professional experience in software engineering, with a focus on backend or platform development
Experience building distributed systems at scale, with a focus on performance tuning, observability, and reliability best practices
Experience with scalable data storage systems (e.g. MySQL, Redis) and optimizing data access and caching for high-throughput applications
Proficiency in one or more server-side programming languages such as Java, Scala, or Python
Experience in feature engineering, integrating AI/ML models into production systems, and understanding model behavior, performance, and constraints
Bachelor's or Master’s degree in Computer Science, Engineering, or a related technical field, or equivalent industry experience

Nice to Haves

Experience building AI agents and components such as memory, context engineering, retrieval, and orchestration
Experience working in cross-functional teams alongside ML engineers, data scientists, and product stakeholders to bring AI/ML products to production
Experience with containerization tools like Docker and Kubernetes, and deploying applications in cloud environments such as AWS or GCP

Design and evaluate architecture solutions for AI infrastructure
Explore, benchmark, and integrate new AI/ML tools and technologies
Build and maintain scalable, reusable backend services for real-time AI/ML inference
Collaborate closely with ML engineers, data engineers, and data scientists
Take end-to-end ownership of system reliability and operational excellence
Continuously grow technical and interpersonal skills

Annual paid time off and generous paid leave scheme
Hybrid working including flexible arrangements and work from abroad opportunities
On-site perks like meals, coffee, snacks, and multi-faith rooms
Commuting allowance and bike reimbursement
Discounts, wallet credits, and access to development programs
Global Employee Assistance Program and free Headspace membership