We are pleased to announce that Imagine AI has joined Metheion as a Technical Recruiting partner.
Making AI-generated software production-ready
Artificial intelligence has fundamentally changed the economics of software development. Code can now be generated faster and at a fraction of the previous cost. However, the ability to produce code does not automatically mean the ability to verify its quality, understand its architecture, or ensure that it will survive real-world production conditions.
Companies increasingly face a different engineering challenge: determining whether the software they are developing is reliable, secure, maintainable, and production-ready.
Imagine AI works with companies facing these challenges. Bringing together experienced CTOs and engineering practitioners, the company provides independent technical judgment on critical software decisions and takes responsibility for software delivery when a project requires more than advice.
Its approach centers on two things: delivering a technical verdict that companies may struggle to reach internally, and taking ownership of the implementation when a decision needs to become a working production system.
Five practitioners, one engineering standard
The Imagine AI team combines experienced technical leadership with AI-assisted software development. Partners provide architectural judgment, make business-critical technical decisions, and own agreed outcomes. AI operators specify tasks, run AI tools, review and correct generated code, and integrate the resulting components.
AI handles most of the code generation. Human practitioners remain responsible for architecture, engineering judgment, quality control, and production readiness.
The team includes:
Slava Rosin, CTO of AxlBrains
Robert Kaev, CTO of Foxway Group
Dr. Artur Jutman, CEO of Testonica Lab
Vladimir Ivanov, Co-Founder and CTO of Supplied, formerly at Bolt
Dalibor Pap, Senior Product Designer at Foxway Group
Decisions into production delivery
Imagine AI structures its work around three stages:
Decide. Deliver the technical verdict or conduct a deeper assessment of an existing system, another team's development plan, or a project that requires rescuing. The objective is to establish what should be built, changed, or left alone.
Deliver. Build new software, modernize existing systems, or take over stuck projects. A named partner runs the work, with a go/no-go decision at each phase.
Sustain. Stabilize and document the delivered system, transfer knowledge, and ensure the client's own engineering team can operate it independently.
Metheion will support Imagine AI with technical recruiting across
Software Architects
Specialists in system architecture, distributed systems, technology selection, legacy modernization, scalability, and technical risk assessment.Principal Software Engineers
Senior engineers capable of reviewing complex codebases, establishing engineering standards, identifying architectural weaknesses, and guiding critical implementation decisions.Backend Software Engineers
Engineers experienced in API development, databases, distributed services, application architecture, performance optimization, and production software.Full-Stack Software Engineers
Developers capable of working across frontend, backend, APIs, databases, and system integration to deliver complete software products.AI Software Engineers
Engineers experienced in integrating large language models, AI APIs, automated development workflows, agentic systems, and AI-powered software applications.DevOps and Platform Engineers
Specialists in cloud infrastructure, CI/CD, containerization, deployment automation, infrastructure as code, and production environments.Site Reliability Engineers
Engineers focused on system reliability, observability, performance, incident management, load testing, and production resilience.QA Automation Engineers
Specialists in automated testing, integration testing, regression testing, test architecture, and verification of AI-generated software.Application Security Engineers
Engineers experienced in secure software architecture, vulnerability assessment, application security testing, dependency management, and data protection.Data Engineers
Specialists in data architecture, database systems, data pipelines, data integrity, and the reliability of data-intensive applications.Technical Product Designers
Designers experienced in SaaS products, UX architecture, design systems, product workflows, and interfaces for increasingly complex software applications.
The code is no longer a bottleneck, it is whether it can safely reach production.
AI-assisted development introduces a different set of challenges for companies building and maintaining critical software systems. Code generation is becoming increasingly accessible, but ensuring that the resulting software behaves correctly under real operating conditions remains a substantial engineering task.
Architecture and system understanding. AI-generated code can introduce architectural inconsistencies, duplicated logic, and dependencies that are difficult to maintain. Engineers must understand how individual components interact with the wider system and whether the overall architecture remains appropriate as the software evolves.
Code quality and verification. Faster code generation increases the volume of changes that need to be reviewed and tested. Teams must establish reliable verification processes covering correctness, maintainability, security, dependencies, and compatibility with existing systems.
Production reliability. Software that works in a development environment may behave very differently under real production loads. Performance bottlenecks, concurrency issues, resource constraints, infrastructure failures, and unexpected user behavior can expose weaknesses that were not apparent during development.
Security and data integrity. Software must handle sensitive information correctly, enforce appropriate access controls, protect against vulnerabilities, and preserve data integrity. These requirements become particularly important when AI-generated components interact with existing databases, external services, and business-critical systems.
Legacy modernization. Replacing an aging system is rarely as simple as rewriting its code. Existing applications often contain years of accumulated business logic, integrations, and undocumented workflows. Successful modernization requires understanding what must be preserved, what should change, and how to migrate without disrupting ongoing operations.
Release management and ownership. Getting software into production requires more than completing development tasks. Testing, deployment, monitoring, documentation, rollback procedures, and knowledge transfer all contribute to whether a release can be considered successful.
These challenges are interconnected. Faster implementation can increase the need for review, architectural changes can introduce unexpected dependencies, and modernization can expose business logic that was never properly documented.
Solving these problems requires engineers who understand not only how to build software, but also how to evaluate, validate, deploy, and maintain it.
Finding engineers who can take responsibility beyond writing code
Imagine AI approach creates a specific need for engineers who can do more than develop features. For Metheion, the task is to identify people with the right combination of technical depth and practical experience, rather than simply searching for candidates who list AI development tools on their CVs. We use technical interviews to examine how engineers approach real problems, evaluate engineering decisions, and apply their knowledge beyond familiar tools or frameworks.
The relevant experience is often found across different engineering environments. Software architects may bring a system-level perspective, principal engineers the ability to solve complex implementation problems, while DevOps, QA, and security specialists ensure that the resulting software operates reliably in production.
Through this partnership, we aim to help Imagine AI bring together the engineering expertise needed to make AI-assisted software development work at production scale.
