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)