Software Engineer

Introduction: DataProphet is a global leader in Artificial Intelligence (AI) for manufacturing. Our award winning technology embeds unique adaptations and advancements of deep learning, enabling AI to have a significant, practical, impact on the factory floor. DataProphet’s solutions are built to be adapted and integrated into existing environments, making it possible for our digital transformation team to take your operations from zero to AI. We understand manufacturing and that real impact is achieved with pre-emptive actions because real-time is often too late. For more information, visit www.dataprophet.com Why join DataProphet? You'll work on technically challenging problems where AI moves beyond experimentation and creates measurable real-world impact. You'll have meaningful ownership, work alongside highly capable colleagues across Data Science and Engineering, and have the opportunity to apply your skills across new problems, use cases and domains. Curiosity, continuous learning and collaboration are central to how we work. Our team works together from our DeWaterkant, Cape Town office in a professional, supportive environment designed to help people do their best work. Role Overview: We are looking for a Software Engineer to design, build and evolve the software that enables DataProphet's AI solutions to operate reliably in complex, real-world environments. You will own meaningful technical problems end-to-end — from understanding requirements and designing solutions through to implementation, testing, deployment and production support. Roles and responsibilities will include, but are not limited to: Design, build, test and maintain high-quality production software. Own features and components end-to-end, from technical design through deployment and post-release support. Build reliable services, APIs, tooling and integrations that support DataProphet's AI products and client environments. Translate loosely defined requirements into clear technical plans and effective software solutions. Contribute to system and component-level architecture and design decisions. Write clean, maintainable, secure and well-tested code. Participate actively in code reviews and contribute to improving engineering standards and practices. Diagnose and resolve production issues and improve system reliability, performance and observability. Collaborate closely with Data Scientists, Data Engineers and other technical and business stakeholders. Communicate technical decisions, risks and trade-offs clearly. Support and contribute to the development of less experienced engineers. Qualifications & Experience: Bachelor's / Honours / Master’s / PhD degree in Computer Science, Software Engineering, Information Systems, or a related field 2–5 years building and maintaining production software infrastructure Demonstrated experience owning software features or components through design, implementation, testing and deployment. Experience building and supporting software in production environments. Experience working collaboratively within a software engineering team. Relevant technical or cloud certifications are advantageous (i.e., AWS Data Engineer, GCP Professional Data Engineer, Azure Data Engineer Associate) Core Skills: Strong proficiency in one or more modern programming languages such as Python, Javascript, Golang or Elixir Ability to write clean, maintainable, secure and testable production-quality code. Strong understanding of software engineering principles, data structures and software design. Solid component-level system design capability, including APIs, services, data flows, observability, monitoring, performance and security. Experience building & debugging distributed systems. Strong testing discipline, including unit and integration testing. Experience with version control and collaborative development practices such as Git and code review. Familiarity with CI/CD and modern software development and deployment practices. Experience working with databases and data persistence technologies (such as PostgreSQL, SQLite, DuckDB, MSSQL, Clickhouse, Amazon S3, etc) . Exposure to cloud platforms such as AWS, Azure or GCP, containerisation technologies such as Docker, Kubernetes, and cloud-native development practices is advantageous. Strong familiarity with Linux, shell scripting and networking concepts. Ability to break down complex or loosely defined problems into effective technical solutions. Ability to communicate technical decisions and trade-offs clearly to both technical and non-technical stakeholders.