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 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)