# AI Engineer > CodeNinja · Lahore, Pakistan · — · Posted 2026-10-14 **Workplace:** on_site **Department:** Professional Services ## Description **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** to design, develop, and operate **AI-powered software systems** that deliver immediate, real-world value. This role sits at the intersection of **software engineering, product engineering, and applied AI**, with a clear mandate: use AI to materially improve how software is built and what it can do. This is **not a research or theoretical machine learning role**. You will focus on practical implementation — building and integrating **agentic systems, model-driven workflows, and AI-enabled capabilities** directly into production applications, internal workflows, and customer-facing products. As CodeNinja advances its AI-first way of working, you will be a highly autonomous engineer, augmented by intelligent systems, treating **models, agents, and context as first-class runtime components** to improve leverage, quality, and speed in the systems you own. **Key Responsibilities** ### AI Engineering & Development - Design, build, and operate **production-grade software systems** that embed AI as a core capability rather than an add-on. - Build and integrate **agentic systems** that autonomously select tools, manage context, and execute multi-step tasks toward defined goals. - Implement **Model Context Protocol (MCP)** or equivalent mechanisms to manage context, tools, memory, and system boundaries in production. - Translate product requirements into AI-enabled implementations that are **scalable, reliable, secure, and observable**. - Apply AI through **prompting, orchestration, model composition, evaluation, and deployment**, focusing on runtime behavior rather than model training. - Treat model inputs, outputs, and behavior as **versioned, testable components** of the system. ### AI-First Software Development - Use AI directly in daily engineering work, including: - AI-assisted and AI-generated code - Autonomous and semi-autonomous engineering agents - AI-driven test generation, debugging, and refactoring - AI-enabled documentation and observability - Treat AI as a default everyday tool to increase personal and team leverage. - Iterate on prompts, policies, agent flows, and contextual inputs as part of normal development workflows. - Apply and refine AI-enabled development practices to **reduce manual effort and improve delivery quality**. ### Product & Platform Collaboration - Partner closely with Product, Scrum Masters, and engineers to deliver AI-powered features **end to end** — and take on these roles when needed, including working as a **single-person team**. This is **true full-stack in an AI-powered environment**. - Integrate AI systems with real-world **services, APIs, and data sources** in production applications. - Contribute to shared platforms, services, and internal tooling in line with established patterns and architectural direction. - Balance rapid iteration with sound engineering judgment and long-term maintainability. ### Quality, Reliability & Accountability - Own the quality of your deliverables across the full stack; partner with QA when available, but **build AI agents that guarantee the quality of what you ship**. - Ensure AI-enabled systems meet **enterprise standards** for security, performance, reliability, and compliance. - Implement **safeguards, monitoring, and evaluation** for AI behavior in production, including detection of hallucinations, regressions, and unintended behavior. - Treat evaluation, traceability, and explainability as ongoing engineering concerns. - Own solutions from initial design through deployment and ongoing operation. ### Continuous Improvement & Learning - Stay current on AI tools, platforms, models, and agentic patterns relevant to real-world software. - Experiment responsibly with new AI capabilities and turn learnings into concrete improvements. - Contribute to a culture of technical excellence, accountability, and continuous learning. ## Requirements ### Skills & Expertise Required - Bachelor's degree in **Computer Science, Engineering**, or a related technical field, or equivalent practical experience. - Strong **software engineering fundamentals**, with experience building and operating production systems. - **Hands-on experience building or working with real agentic AI systems**, including giving models autonomy to select tools and execute multi-step tasks. - **Experience using Model Context Protocol (MCP)** or equivalent approaches to manage tools, context, and memory in production AI systems. - **Solid technical understanding of AI models**, including: - Model names and versions - Parameter sizes and context window limits - System prompts vs. user prompts - Trade-offs between models (latency, cost, reasoning quality) - **Experience with open-source AI models and ecosystems**, alongside commercial or hosted models. - Experience integrating AI systems with **real-world services, APIs, and data sources**. - Ability to evaluate AI systems beyond accuracy — including **reliability, hallucination risk, traceability, and safety**. - Experience addressing scaling concerns such as **latency, cost optimization, caching, parallelization**, and RAG vs. fine-tuning trade-offs. - Proven ability to take AI systems from **prototype to pilot to production**, with awareness of common failure modes and how to mitigate them. - Comfortable working autonomously in small, highly empowered teams. ### Preferred Skills - Experience integrating frontier and open-source models into **SaaS or enterprise applications**. - Familiarity with **cloud-native architectures** and modern development stacks. - Experience in regulated, enterprise, or mission-critical environments. - Background in product-oriented engineering organizations. ### Why Join Us - Build AI systems that run in production — and apply AI where it actually counts. - Work with cutting-edge tools, models, and large-scale enterprise systems. - Opportunity to work with global engineering teams. - Culture focused on learning, innovation, ownership, and technical excellence. ### Equal Opportunity Statement At **CodeNinja**, we are an **Equal Opportunity Employer**. We value diversity and are committed to fostering an inclusive environment for all employees — regardless of nationality, gender, or background. ## Benefits - **Provident Fund (PF)** - **Medical Insurance (Pakistan):** Coverage includes spouse and parents up to the age of 80. - **Life Insurance**  - **Performance-Based Increment** - **32 Paid Leaves (Annual)** - **Easy Loan Facility** - **Company-Sponsored Trips** - **Gym Passport Membership (Access To 250+ Gyms)** ## Apply [Apply at CodeNinja](https://apply.workable.com/codeninjapk/j/52782C6B75/apply) --- Powered by [Workable](https://www.workable.com)