• Monolith vs services.
• Synchronous vs asynchronous communication.
• SQL vs NoSQL.
• Build vs buy.
• Short-term delivery vs long-term technical sustainability.
• Remain hands-on with Python and FastAPI through critical implementation, prototyping, debugging, code reviews, and technical research.
• Lead the design of event-driven and asynchronous workflows, including retries, idempotency, ordering, failure handling, and message processing.
• Own backend production readiness and operational health, including monitoring, logging, alerting, dashboards, SLIs/SLOs, runbooks, incident response, root-cause analysis, and post-incident improvements.
• Establish clear ownership of backend services and ensure the team can support them without dependency on individual engineers.
• Improve engineering practices covering refinement, technical design, Definition of Done, testing, code review, CI/CD, release safety, documentation, and technical debt management.
• Introduce and scale AI-assisted and agentic development workflows across the backend team.
• Evaluate how AI tools affect delivery speed, engineering quality, maintainability, and team productivity.
• Lead and develop backend engineers through delegation, regular feedback, one-to-one meetings, mentoring, performance reviews, and career development.
• Collaborate closely with Product, Platform, DevOps, QA, Frontend, and other engineering leads.
• Take responsibility for product and business outcomes, not only technical implementation.

What We’re Looking For:

• 7+ years of backend engineering experience.
• 2+ years of experience in a Tech Lead, Team Lead, or Backend Lead role.
• Strong and recent hands-on production experience with Python, ideally 5+ years.
• Strong knowledge of modern Python backend development practices.
• Production experience with FastAPI or similar Python web frameworks.
• Experience with asynchronous programming, testing, typing, profiling, debugging, and maintainable application design.
• Proven experience owning backend architecture end to end, not only contributing to solutions designed by others.
• Experience making and defending architectural decisions with Product, Engineering, and business stakeholders.
• Experience building and operating scalable, high-load, or data-intensive production systems.
• Strong understanding of service design, system boundaries, data flows, integrations, and distributed system trade-offs.
• Strong experience with event-driven systems, asynchronous processing, and message brokers such as Kafka, RabbitMQ, SQS, or similar.
• Good understanding of retries, idempotency, message ordering, delivery guarantees, dead-letter handling, and failure recovery.

Strong database fundamentals, including:

• Data modelling.
• Transactions and isolation.
• Indexing.
• Locking and concurrency.
• Query plans.
• Performance diagnosis and optimisation.
• Production experience with MongoDB and an understanding of its modelling, consistency, and scaling tradeoffs.
• Production experience with relational databases, preferably PostgreSQL.
• Experience designing APIs and integrating external platforms, third-party services, and payment or subscription systems.
• Strong production ownership mindset, including monitoring, alerting, debugging, incident investigation, and reliability improvements.
• Experience with cloud infrastructure, containerised applications, deployment tooling, and CI/CD.
• Ability to reason about deployment safety, rollbacks, scalability, resource limits, and system capacity.
• Proven experience improving an engineering process, technical area, or system from an unclear or immature state to a measurable target state.
• Ability to balance product delivery speed with architecture quality and long-term maintainability.
• Experience leading experienced engineers without micromanagement.
• Experience with delegation, performance feedback, team development, and technical ownership distribution.
• Strong ownership, initiative, structured communication, and ability to make decisions under uncertainty.

AI-First Engineering Experience:

We are looking for more than occasional chatbot usage or basic code generation. A strong candidate should be able to provide concrete examples of using AI in an end-to-end software delivery workflow, such as:

• Spec-driven or context-driven development.
• AI coding agents.
• Automated code review.
• Test and documentation generation.
• Reusable repository or company-level AI context.
• Agentic pipelines and workflow automation.
• AI-assisted debugging and incident investigation.
• Evaluations and quality gates.
• Security and data privacy controls.
• Measuring the impact of AI adoption on delivery speed and quality.
• Introducing AI workflows across an engineering team.

Nice to Have:

• Experience building multi-product, multi-tenant, or platform-based architectures.
• Experience creating internal frameworks, service templates, or reusable backend components.
• Experience scaling products without proportional growth of the engineering team.
• Experience with AWS, including S3, Lambda, ECS, EKS, or related services.
• Experience with Redis.
• Hands-on Kubernetes experience.
• Strong Docker experience.
• Experience with GitHub Actions or similar CI/CD tooling.
• Experience with Temporal or another workflow orchestration platform.
• Experience applying DDD, CQRS, event sourcing, or similar architectural patterns pragmatically.
• Experience with B2C, EdTech, subscriptions, payments, analytics, or mobile-first products
• Experience working in an early-stage or fast-changing product environment.

Good Fit for This Role:

This role is a good fit for someone who:

• Remains a strong hands-on backend engineer.
• Enjoys combining coding, architecture, and leadership.
• Can independently assess the current technical state and define a better target state.
• Creates engineering processes rather than only following existing ones.
• Takes ownership of services after they are released to production.
• Can make pragmatic architectural decisions without unnecessary complexity.
• Thinks about product and business outcomes, not only implementation.
• Can set a technical standard for experienced engineers.
• Is comfortable challenging existing decisions and defending a well-reasoned position.
• Sees AI as a way to redesign engineering workflows, not only generate code.
• Is ready to lead both technology and people.

Why You’ll Love Working With Us:

• Innovative environment: We are building and scaling EdTech products while actively experimenting with new technologies and development approaches.
• Global remote work: Work with an international team from anywhere within a compatible time zone.
• Health support: Company-provided medical expense compensation.
• AI tools: Company-provided AI subscriptions and other engineering tools.
• Flexible time off: 21 days of annual leave plus 10 bank holidays.
• Collaborative culture: Work alongside experienced and motivated professionals who value ownership, speed, and measurable results.
• Real influence: Help define the backend strategy, engineering standards, and AI-first development practices of a growing product ecosystem.

Interview Process:

1. HR interview
2. Technical Interview
3. Leadership Interview
4. Final interview