Senior AI Engineer

CodeNinja is a global AI and engineering services company that supports enterprises in building, scaling, and operating intelligent systems. With over 350 engineers across four continents and more than 400 successful deployments, CodeNinja empowers over 250 clients worldwide to leverage artificial intelligence through its Global Capability Centers, Work AI, Physical AI, and AI Labs. Recognized as one of Pakistan’s fastest-growing AI firms and a multi-award recipient on Clutch, the company drives innovation, automation, and competitiveness in the intelligence economy.

We are seeking a highly experienced Senior AI Engineer to join our team as a senior individual contributor focused on solving complex, high-impact problems through the applied use of AI in production software systems. This role goes beyond execution; you will define how AI is built and integrated at CodeNinja by establishing implementation patterns, quality standards, and technical guardrails that scale across teams. You will lead critical initiatives end to end, navigate ambiguous problem spaces, and make key technical decisions involving models, agentic systems, architecture, and trade-offs. The goal is to deliver AI-enabled systems that meet enterprise expectations for reliability, performance, cost efficiency, and safety.

This position is not focused on research or theoretical machine learning but on applied, production-grade AI. You will design and integrate agentic systems, model-driven workflows, and automation into real software that delivers measurable business and product value. As CodeNinja advances its AI-first approach, you will serve as a pillar 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 addressing complex business challenges. Architect 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 that are 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 problems into clear technical approaches and guide teams through implementation. Identify system-level risks and trade-offs early, addressing them through design and safeguards. Influence technical direction across teams through expertise and judgment. 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 code, agent-based workflows, AI-driven testing, debugging, refactoring, and AI-enabled documentation and observability. Establish standards for agent behavior, prompt and context design, evaluation, regression detection, and safe, explainable AI behavior in production. Ensure AI is embedded as a default capability in engineering workflows.

- 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 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, 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 integrate proven approaches into shared practices. Contribute to a culture of disciplined execution, learning, and technical excellence.

Required Qualifications

- 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.
- Solid technical understanding of AI models, including model versions, parameter scales, context window limitations, system vs. user prompts, and trade-offs related to latency, cost, reasoning quality, and reliability.
- Experience with both frontier and open-source models, and making informed decisions between them.
- Proven ability to scale AI systems considering latency, cost optimization, caching, parallelization, and model selection trade-offs.
- Demonstrated success taking complex AI systems from prototype through production and sustaining them over time.
- Strong judgment and ability to operate effectively in ambiguous problem spaces.

Preferred Qualifications and Benefits

- 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.

Benefits include Provident Fund, Medical Insurance covering spouse and parents up to age 80 (Pakistan), Life Insurance, Performance-Based Increments, 32 Annual Paid Leaves, Easy Loan Facility, Company-Sponsored Trips, and Gym Passport Membership with access to over 250 gyms.

CodeNinja is an Equal Opportunity Employer committed to fostering an inclusive environment for all employees regardless of nationality, gender, or background. Join us to define how agentic AI systems are built and operated in real production environments, lead impactful AI initiatives, and work with cutting-edge tools alongside global engineering teams in a culture that values learning, innovation, ownership, and technical excellence.