Senior AI Engineer

CodeNinja is a leading global AI and engineering services company dedicated to helping enterprises build, scale, and operate 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 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 fosters innovation, automation, and competitive advantage in the intelligence economy.

We are seeking a highly experienced Senior AI Engineer to join our team as a senior individual contributor. This role focuses on solving complex, high-impact problems by applying AI in production software systems. Beyond execution, you will define how AI is built and used 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 strategic technical decisions involving models, agentic systems, architecture, and trade-offs. The goal is to deliver AI-enabled systems that meet enterprise standards for reliability, performance, cost efficiency, and safety. This is an applied AI role centered on production-grade systems, integrating agentic systems, model-driven workflows, and automation into real software that drives measurable business value.

Key Responsibilities

- Design, build, and operate sophisticated AI-enabled software systems addressing 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 environments.
- 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.
- 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, failure modes, and trade-offs early, addressing 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.
- Define, evolve, and share AI usage patterns across engineering teams, including AI-assisted coding, agent-based workflows, AI-driven testing, debugging, refactoring, and documentation.
- Establish standards for agent behavior, prompt and context design, evaluation, regression detection, and safe, explainable AI behavior in production.
- Mentor AI engineers through technical guidance, pairing, design and code reviews, raising AI fluency and engineering rigor across teams.
- Collaborate closely with Product, Design, and engineering peers to deliver AI-powered features end to end, translating product intent into robust technical architectures.
- Communicate complex AI concepts clearly to both technical and non-technical stakeholders, helping product teams understand AI trade-offs and constraints.
- 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 detection, traceability, explainability, and behavioral regression monitoring.
- Stay current on AI tools, platforms, models, and agentic patterns relevant to production systems, evaluating emerging capabilities and integrating proven approaches responsibly.

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.
- Strong technical understanding of AI models, including knowledge of model versions, parameter scales, context window limitations, system vs. user prompts, and trade-offs involving latency, cost, reasoning quality, and reliability.
- Experience with both frontier and open-source AI models and making informed decisions between them.
- Proven ability to scale AI systems with considerations for latency, cost optimization, caching, parallelization, and model selection trade-offs.
- Demonstrated success in 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

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

At CodeNinja, we are an Equal Opportunity Employer committed to fostering a diverse and inclusive work 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 high-impact initiatives, and work with cutting-edge tools alongside global engineering teams in a culture that values learning, innovation, ownership, and technical excellence.