Senior AI Engineer

About the Company
CodeNinja is a global AI and engineering services company helping enterprises build, scale, and operate intelligent systems. With 350+ engineers across four continents and 400+ successful deployments, CodeNinja enables organizations to harness artificial intelligence through Global Capability Centers, Work AI, Physical AI, and AI Labs. Recognized among Pakistan’s fastest-growing AI firms and a multi-award recipient on Clutch, CodeNinja empowers over 250 clients worldwide to innovate, automate, and compete in the intelligence economy.

About the role
We are looking for a hands-on AI Engineer responsible for designing, developing, and operating AI-powered software systems that deliver immediate, real-world value. This role sits at the intersection of software engineering, product engineering, and applied artificial intelligence, with a strong focus on practical implementation rather than research or theoretical model development. As part of CodeNinja’s AI-first operating model, you will leverage LLMs, AI agents, and automation to fundamentally improve how software is built, deployed, and operated.

Key Responsibilities

• Design, build, and operate production-grade software systems with AI embedded as a core capability.
• Apply agentic solutions and automation frameworks to solve concrete business and product challenges.
• Translate product requirements into scalable, secure, and reliable AI-enabled solutions.
• Focus on applied AI usage including prompting, orchestration, integration, evaluation, and deployment.
• Use AI directly in daily engineering work, including AI-assisted code generation, debugging, refactoring, and test creation.
• Continuously evolve AI usage across the software development lifecycle to improve speed, quality, and leverage.
• Collaborate closely with Product, Scrum, and Engineering teams to deliver AI-powered features end to end.
• Contribute to shared platforms, services, and internal tools that enable AI-native development at scale.
• Ensure AI-enabled systems meet standards for security, performance, and reliability.
• Implement monitoring, safeguards, and evaluation mechanisms for AI behavior in production.
• Take full ownership of solutions from design through deployment and ongoing operation.
• Stay current with emerging AI tools, platforms, and implementation patterns.