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 highly experiencedSenior AI Engineer— a senior individual contributor who solves complex, high-impact problems through theapplied use of AI in production software systems. Beyond execution, you will define how AI is built and used at CodeNinja by settingimplementation patterns, quality standards, and technical guardrailsthat scale across teams.

You will lead critical initiatives end to end, operate effectively in ambiguous problem spaces, and make high-leverage technical decisions involvingmodels, agentic systems, architecture, and trade-offs— delivering AI-enabled systems that meet enterprise expectations for reliability, performance, cost efficiency, and safety.

This isnot a research or theoretical machine learning role. The focus is applied, production-grade AI — designing and integratingagentic systems, model-driven workflows, and automationinto 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 operatesophisticated AI-enabled software systemsthat address complex business and product challenges.
• Architect and implementagentic systemscapable of autonomous tool selection, multi-step goal execution, and controlled decision-making in production.
• Define and enforceModel Context Protocol (MCP)patterns for managing tools, context, memory, policies, and system boundaries at scale.
• Make informed decisions onmodel 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 astechnical 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 thelong-term technical healthof 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 adefault capabilityin engineering workflows — not an experiment or side effort.

Mentorship & Capability Building

• Mentor AI Engineersthrough 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 featuresend to end.
• Translate product intent into robust technical architectures, balancing speed, quality, safety, and maintainability.
• Communicate complex AI and agentic concepts clearly totechnical and non-technical stakeholders.
• Help product teams understand AI trade-offs and constraints to drive better decisions.

Quality, Reliability & Accountability

• Ensure AI-enabled systems meetenterprise standardsfor 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, includingcost 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 inComputer Science, Engineering, or a related technical field, or equivalent practical experience.
• Extensive experiencebuilding 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 experiencescaling AI systems— latency, cost optimization, caching, parallelization, and model selection trade-offs.
• Demonstrated ability to take complex AI systems fromprototype through productionand sustain them over time.
• Strong judgment and ability to operate effectively in ambiguous problem spaces.

Preferred Skills

• Experience definingengineering standards, patterns, or platformsadopted by multiple teams.
• Experience shapingAI platform architectureor 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

AtCodeNinja, we are anEqual 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)