• Work closely with the Engineering Manager, Tech Lead, QA Engineer, and Product Manager to help solutionise feature requests • Lead the creation of detailed documentation for new feature developments, incorporating diagrams for comprehensive project understanding. • Drive the development and implementation of advanced tests around new features, ensuring the highest standards of test coverage and reliability. • Demonstrate expertise in producing well-architected, efficient, and scalable code using industry-leading design patterns and methodologies. • Take a leadership role in code reviews and pair programming sessions, guiding and mentoring colleagues towards excellence. • Lead and actively participate in tech-talks, workshops, and team-based learning initiatives, ensuring knowledge is disseminated effectively across the team.

• Design, develop, and maintain backend services using Python and FastAPI. • Build and maintain RESTful APIs and microservices. • Containerize applications using Docker and deploy them to Kubernetes. • Work with Azure cloud services to support scalable and secure solutions. • Contribute to CI/CD pipelines using GitHub and ArgoCD. • Integrate AI/ML or RAG-based components where applicable. • Collaborate closely with other developers, DevOps engineers, and product stakeholders. • Write clean, well-tested, and well-documented code. • Troubleshoot, debug, and resolve production issues. • Follow best practices in security, performance, and maintainability. • Continuously improve existing systems and stay up to date with backend and AI trends.

• Design and develop robust backend services and APIs using Java. • Build and maintain scalable data pipelines and ETL processes. • Develop high-performance SQL queries and optimize database performance. • Implement and manage AWS-based data solutions. • Integrate data across multiple platforms and systems. • Ensure code quality through unit testing, code reviews, and CI/CD best practices. • Monitor, troubleshoot, and optimize production systems. • Collaborate with data engineers, architects, and DevOps teams to deliver secure, scalable solutions.

• Design, develop, and maintain Java-based backend services for ML pipelines. • Collaborate with data scientists to productionize machine learning models. • Build scalable data ingestion, transformation, and processing pipelines. • Implement APIs and microservices to integrate ML models into applications. • Optimize performance of large-scale data systems. • Deploy and manage ML workloads on Kubernetes or cloud platforms. • Ensure code quality, testing, and adherence to software engineering best practices. • Monitor, troubleshoot, and tune ML systems in production.