Building AI products: the engineering behind software quality, AI evaluation, and reliable technology

AI/ML Quality Engineer, Test Team Lead, and founder of TechRise, Nurida Nurmambetova, shares how she built her career across global technology projects, why AI product quality has become essential worldwide, and how she is developing an AI-powered learning ecosystem to support the next generation of tech professionals.

Nurida Nurmambetova, AI/ML Quality Engineer, Test Team Lead, and founder of TechRise, Linkedin 

How it all began

My path into technology started in Bishkek, where I worked on analytical projects and launched my first startups. While building early website prototypes, I became deeply interested in why some systems remain stable under load while others fail, and what engineering principles ensure reliability. That curiosity pushed me toward data analysis, system modeling, and understanding complex architectures long before I formally entered the IT industry.

Later, I joined Coca-Cola Içecek in financial controlling and was selected for the U30 Strategy Camp at the company’s headquarters in Istanbul. It was a competitive leadership initiative that brought together participants from across ten countries, along with senior executives, including Burak Başarır, CEO of Anadolu Group. We worked on long-term digital and technological strategies spanning the next three decades. That experience demonstrated how automation, intelligent systems, and digital architecture can transform entire industries and strengthened my interest in understanding how large-scale technologies function and evolve.

Technical career and engineering leadership

Over time, I expanded my technical skillset and passed a multi-stage, highly selective hiring process to join Deloitte. Working at one of the world’s leading consulting firms exposed me to complex enterprise systems and high-pressure engineering environments. I supported several major initiatives, including the migration of more than 400 corporate applications into Hewlett Packard’s Private Cloud and the development of a QA strategy for modernization efforts within the Edward Jones financial ecosystem. These projects required close collaboration with global engineering teams and careful coordination across multiple interconnected systems. This experience deepened my engineering discipline and shaped my understanding of enterprise-level technology transformations.

Advancing the field of AI product quality

Today, my expertise lies at the intersection of software quality, AI systems, and model behavior -an area that has become essential as AI enters real-world applications. My work includes hands-on AI model training and annotation, data-quality evaluation, and scenario-based assessments that directly influence how AI systems learn and respond. I identify model strengths and vulnerabilities, analyze behavioral inconsistencies, and design validation strategies that ensure safety, accuracy, and reliability at scale.

I lead a distributed team working on a high-visibility AI/ML initiative for a global big tech company developing next-generation smart-glasses. My responsibilities include triaging test cases, reviewing complex outputs, and ensuring that the data feeding the models meets the highest quality standards. I design validation workflows, build internal test frameworks, check the feasibility of scripts and establish evaluation criteria for assessing accuracy, hallucination patterns, and performance under real-world conditions. These contributions directly affect the reliability, user trust, and safety of AI-driven features.

In 2025, I was invited as a Featured Expert and Speaker at the Generative AI Hackathon 3.0, a program that reached more than 570 participants across Kyrgyzstan and Central Asia. My session on building reliable AI-driven products generated significant interest, and I was later invited to serve as an Official Judge for the final pitch day. This recognition reflected my growing involvement in evaluating AI solutions and supporting emerging innovators.

Building an IT and AI learning ecosystem

TechRise emerged from a clear need: many people want to enter the tech industry but lack structured guidance, real engineering exposure, and practical learning tools. I created TechRise as a modern, AI-integrated ecosystem that supports career development.

The platform combines AI-powered learning and personalized guidance with structured career roadmaps, real industry case studies, and a mentoring community that helps learners grow in a practical, results-driven way. Since launch, the TechRise platform has attracted more than 500 visitors, showing strong early demand from individuals seeking structured, AI-informed pathways into tech. We have already hosted workshops, educational sessions, and discussions that help newcomers gain real skills and understand modern industry requirements.

TechRise has secured early-stage investment, enabling us to expand learning modules, strengthen AI-driven tools, and grow the community further. This support allows us to accelerate development and reach more learners who need accessible and practical entry points into technology. Ultimately, TechRise reflects my core mission: making AI and tech education accessible, industry-aligned, and grounded in the engineering practices used by global tech teams.

Plans

In the coming years, I plan to continue advancing my work in AI reliability, software quality assurance, and global technological initiatives. My goal is to help shape best practices for building trustworthy AI systems, a need that is becoming increasingly critical as organizations adopt AI at scale.

The TechRise platform will continue expanding its offerings, enhancing its learning tools, and developing diagnostic features that help learners assess their skills and build personalized roadmaps in QA, software development, AI, and product fields. I will continue sharing my knowledge through community programs, workshops, and mentorship.

Beyond education, I intend to build AI-powered products that improve decision-making, automate complex workflows, and raise overall standards for digital system quality. My long-term vision is to contribute to a future where AI systems are reliable, ethical, and safe and where people can confidently rely on the technology that supports their daily lives.

Through these initiatives, I aim to drive innovation, strengthen the global AI ecosystem, and support the next generation of professionals who will shape the technologies of tomorrow.