AI Software Engineer
Our Clients
- Mandaluyong City, Metro Manila
- Permanent
- Full-time
- Full-Stack Design: Design and implement comprehensive technology stacks for AI-based or AI-powered software applications.
- Production Integration: Seamlessly integrate AI technologies, including frameworks, libraries, and models, into production-ready software.
- Pipeline Development: Build secure AI pipelines and back-end services using modern algorithms and methodologies.
- Front-End Interaction: Develop front-end components and modules that interact directly with AI services and data stores.
- End-to-End Execution: Support the full application lifecycle, including development, packaging, and deployment.
- DevSecOps Collaboration: Contribute to CI/CD pipelines and build/release engineering workflows.
- Architecture & Standards: Adhere to established architectural patterns, coding standards, and security guardrails while participating in rigorous code reviews.
- System Integrity: Ensure clean integration between user interfaces, backend services, and databases.
- Applied Research: Stay current on the latest AI technologies to research, design, and deliver end-to-end solutions.
- Global Partnership: Collaborate with stakeholders and global teams to ensure all technical solutions align with broader business goals.
- Documentation: Maintain and update technical documentation to ensure software applications are easily understood and extensible.
- Degree: Bachelor's or Master's degree in Engineering, Computer Science, Artificial Intelligence, IT, or a related field.
- Software Development: Demonstrable experience as a software developer with proficiency in languages such as C++, C#, Python, or Java.
- Design Patterns: Familiarity with applying software design patterns, including SOLID or Gang of Four (GoF).
- AI Expertise: Strong understanding of machine learning algorithms, generative AI, and general AI concepts.
- Framework Proficiency: Experience with AI libraries and frameworks such as PyTorch or TensorFlow.
- Data Engineering: Knowledge of data pre-processing, feature engineering, prompt engineering, and model evaluation techniques.
- AI Tooling: Experience with frameworks like Haystack, LangChain, or Ollama.
- Modern Architectures: Familiarity with REST APIs, asynchronous systems, and distributed enterprise platforms.
- Responsible AI: Knowledge of security, bias mitigation, and responsible AI frameworks.
- Methodologies: Experience with Agile, Test-Driven Development (TDD), and DevSecOps best practices.