AI Product Engineer

Position Summary

Build and deliver AI-enabled digital products from concept to production. The role owns end-to-end solution delivery, leveraging AI-assisted engineering to accelerate development while maintaining enterprise-grade quality, security, and scalability.

Key Responsibilities

• Translate business requirements into production-ready AI solutions.
• Design, develop, test, deploy, and support full-stack applications independently.
• Leverage AI development tools (Cursor, GitHub Copilot, Claude Code, ChatGPT, Gemini) to improve engineering productivity.
• Develop solutions using LLMs, AI Agents, RAG, MCP, APIs, workflow automation, and vector databases.
• Design scalable architectures, integrations, and reusable components.
• Ensure security, code quality, testing, documentation, and operational excellence.
• Collaborate with Product, Business, UX, and Delivery teams.
• Continuously evaluate emerging AI technologies and recommend adoption.

Qualifications

• Bachelor's degree or higher in Computer Engineering, Computer Science, Information Technology, Software Engineering, Artificial Intelligence, or related discipline.
• 3–5 years of professional experience in Software Engineering, AI Engineering, or Full-Stack Development.
• Strong proficiency in Python and/or JavaScript/TypeScript.
• Experience with REST APIs, SQL/PostgreSQL, Git, Docker, CI/CD, and cloud platforms (AWS, Azure, or Google Cloud).
• Hands-on experience with AI-assisted software development is highly preferred.
• Knowledge of LLMs, Prompt Engineering, RAG, MCP, AI Agents, and Vector Databases is an advantage.
• Strong communication, analytical thinking, and stakeholder management skills.
• Self-driven with strong ownership and delivery mindset.

Success Metrics (KPIs)

• Feature lead time: typically less than 5 working days for standard features.
• Deployment frequency: weekly or higher.
• First-pass business acceptance above 90%.
• Critical production defects minimized through testing and reviews.
• High adoption of AI-assisted development to improve productivity.
• Complete technical documentation for delivered solutions.

Interview Assessment

• Technical coding assessment
• System design and architecture discussion
• AI solution design case study
• Business problem-solving case
• Behavioural interview focused on ownership and collaboration