• Database design and implementation of the data services layers. • Analyzing performance problems and making recommendations for optimization where appropriate • Designing and developing custom business solutions from end-to-end based on requirements and client needs. • Interfacing with the internal business team to define requirements; develop code and unit test • Leading/participating in design and code reviews

Develop scalable and reliable systems in an environment that values autonomy, continuous learning, and high-quality engineering.

• Design, develop, and maintain scalable, high-performance REST APIs using Django REST Framework. • Collaborate closely with front-end developers, other back-end developers, product owners, scrum master, solution architect, QA, and other team members to define and implement requirements. • Optimize and refactor existing codebases for better performance, reliability, and security. • Ensure the codebase follows best practices, including testing, code reviews, and documentation. • Integrate third-party services and APIs as required, ensuring data consistency and reliability. • Maintain and improve back-end data structures, managing databases and optimizing queries.

• Design and build backend-heavy platform features for our platform • Productionalise AI-enabled capabilities (e.g. anomaly detection, recommendations, agent-based workflows) • Implement AI thoughtfully across the entire SDLC - prototyping, testing, iteration, and deployment • Collaborate closely with Product to turn vision into shipped features • Identify blockers early, communicate clearly, and iterate fast • Help shape engineering standards and patterns as the product matures • Design systems that support model evaluation, prompt/version management, and deterministic fallbacks to ensure AI-driven features are observable, testable, and production-safe • Build AI features with explicit evaluation criteria, feedback loops, and guardrails (accuracy, latency, cost, and explainability) so models improve predictably over time • Success in the first 6–12 months looks like: 2+ production-ready features shipped. Tangible progress towards operating as a smart intelligence platform. Clear, repeatable engineering patterns for AI-enabled development. Utilize lightweight but rigorous AI engineering practices (evaluation harnesses, rollout strategies, and rollback mechanisms) that allow the platform to scale AI features safely and repeatedly.