AI Engineer

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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.
  

• 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.
  

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.