# Senior AI Engineer > CodeNinja · Lahore, Pakistan · — · Posted 2026-10-14 **Workplace:** on_site **Department:** Engineering ## 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 highly experienced **Senior AI Engineer** — a senior individual contributor who solves complex, high-impact problems through the **applied use of AI in production software systems**. Beyond execution, you will define how AI is built and used at CodeNinja by setting **implementation patterns, quality standards, and technical guardrails** that scale across teams. You will lead critical initiatives end to end, operate effectively in ambiguous problem spaces, and make high-leverage technical decisions involving **models, agentic systems, architecture, and trade-offs** — delivering AI-enabled systems that meet enterprise expectations for reliability, performance, cost efficiency, and safety. This is **not a research or theoretical machine learning role**. The focus is applied, production-grade AI — designing and integrating **agentic systems, model-driven workflows, and automation** into real software that delivers measurable business and product value. As CodeNinja advances its AI-first way of working, you will be an anchor of technical excellence, ensuring AI adoption is pragmatic, scalable, safe, and consistently effective. ### **Key Responsibilities** ### Advanced AI Engineering & System Design - Design, build, and operate **sophisticated AI-enabled software systems** that address complex business and product challenges. - Architect and implement **agentic systems** capable of autonomous tool selection, multi-step goal execution, and controlled decision-making in production. - Define and enforce **Model Context Protocol (MCP)** patterns for managing tools, context, memory, policies, and system boundaries at scale. - Make informed decisions on **model selection, composition, and deployment**, balancing reasoning quality, latency, cost, and reliability. - Treat model behavior, agent flows, and contextual inputs as core system components — **designed, versioned, tested, and observable**. ### Technical Leadership & Initiative Ownership - Serve as **technical lead for high-impact AI initiatives**, owning architecture, execution strategy, and delivery outcomes. - Break down ambiguous, open-ended problems into clear technical approaches and guide teams through implementation. - Identify system-level risks, failure modes, and trade-offs early, and address them through design and safeguards. - Influence technical direction across teams through expertise, judgment, and example. - Own the **long-term technical health** of AI-enabled systems beyond initial delivery. ### AI-First Development Practices & Standards - Define, evolve, and share patterns for effective AI usage across engineering teams, including: - AI-assisted and AI-generated code - Agent-based engineering workflows - AI-driven testing, debugging, and refactoring - AI-enabled documentation and observability - Establish standards for: - Agent behavior and autonomy - Prompt and context design - Evaluation and regression detection - Safe and explainable AI behavior in production - Ensure AI is embedded as a **default capability** in engineering workflows — not an experiment or side effort. ### Mentorship & Capability Building - **Mentor AI Engineers** through technical guidance, pairing, design reviews, and code reviews. - Raise the bar on AI fluency, engineering rigor, and system thinking across teams. - Share patterns, lessons learned, and failure analyses to accelerate collective improvement. - Act as the go-to technical resource for complex AI and agentic system questions. ### Product & Cross-Functional Collaboration - Partner closely with Product, Design, and engineering peers to deliver AI-powered features **end to end**. - Translate product intent into robust technical architectures, balancing speed, quality, safety, and maintainability. - Communicate complex AI and agentic concepts clearly to **technical and non-technical stakeholders**. - Help product teams understand AI trade-offs and constraints to drive better decisions. ### Quality, Reliability & Accountability - Ensure AI-enabled systems meet **enterprise standards** for security, performance, reliability, and compliance. - Design and implement evaluation, monitoring, and safeguard strategies for AI in production, including: - Hallucination and error detection - Traceability and explainability - Behavioral regression and drift - Own outcomes and long-term system health, including **cost efficiency and operational stability**. ### Continuous Improvement & Learning - Stay current on AI tools, platforms, models, and agentic patterns relevant to production systems. - Evaluate emerging AI capabilities and responsibly bring proven approaches into shared practices. - Contribute to a culture of disciplined execution, learning, and technical excellence. ## Requirements ### Skills & Expertise Required - Bachelor's degree in **Computer Science, Engineering**, or a related technical field, or equivalent practical experience. - **Extensive experience** building and operating complex production software systems. - **Deep, hands-on experience designing and operating agentic AI systems in production**, including autonomous tool use and multi-step workflows. - **Strong mastery of Model Context Protocol (MCP)** or equivalent approaches, including defining standards and patterns used by others. - **Strong technical understanding of AI models**, including: - Model names, versions, and parameter scales - Context window limitations and strategies - System prompts vs. user prompts - Trade-offs between models (latency, cost, reasoning quality, reliability) - **Experience with both frontier and open-source models**, and making informed decisions between them. - Proven experience **scaling AI systems** — latency, cost optimization, caching, parallelization, and model selection trade-offs. - Demonstrated ability to take complex AI systems from **prototype through production** and sustain them over time. - Strong judgment and ability to operate effectively in ambiguous problem spaces. ### Preferred Skills - Experience defining **engineering standards, patterns, or platforms** adopted by multiple teams. - Experience shaping **AI platform architecture** or shared agentic frameworks. - Familiarity with cloud-native, SaaS, or platform-oriented environments. - Background in regulated, enterprise, or mission-critical systems. ### Why Join Us - Define how agentic AI systems are built and operated — in real production environments. - Lead high-impact AI initiatives and shape engineering standards across teams. - Work with cutting-edge tools, models, and 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/EBE1CF0143/apply) --- Powered by [Workable](https://www.workable.com)