Tom Smykowski is a remote AI engineer and product-minded technical leader with nearly two decades of experience delivering software from discovery through production. He is the ideal hire for founders, CTOs, and executives seeking an engineer who can take full ownership of AI initiatives, combining hands-on development with strategic product thinking. Based in Poland, he works seamlessly with remote teams across the EU, UK, and US, including overlapping with US East Coast time zones.
What Does a Remote AI Engineer Do?
A remote AI engineer specializes in designing, building, and integrating artificial intelligence and machine learning systems within a distributed team context. This role goes beyond just model training. It encompasses the entire product lifecycle, from understanding business requirements and data strategy to implementing scalable, production-ready AI solutions and maintaining them in live environments. The remote aspect requires exceptional communication skills, proactive collaboration, and a disciplined approach to asynchronous work.
For teams looking to hire, a remote AI engineer like Tom brings a unique blend of deep technical expertise in modern AI tooling and frameworks, coupled with a strong product mindset. This ensures that AI features are not just technically impressive but are also user-focused, commercially viable, and aligned with business goals. His experience spans from setting up AI software factories to integrating LLMs into existing SaaS platforms, always with an eye on maintainability and scalability.
The value of hiring a seasoned remote AI engineer is clear. They can accelerate your AI roadmap, de-risk complex integrations, and upskill your existing team. Tom's approach involves close collaboration with product managers, designers, and stakeholders to turn ambiguous challenges into concrete, deployable solutions, whether you need an MVP, a system modernization, or a full-scale AI product strategy.
AI Engineering Expertise and Project Experience
Tom's AI engineering expertise is built on a foundation of full-stack software development and product leadership. His work involves implementing AI-assisted workflows, agentic engineering principles, and AI-native software development lifecycles. He is proficient with tools like Claude Code, Cursor, and GitHub Copilot to enhance developer productivity and code quality. This practical knowledge is critical for teams aiming to adopt AI not just in their products but within their engineering processes.
His project portfolio demonstrates a consistent ability to tackle complex, high-scale problems. At Printify, he led the frontend design system and rebrand ahead of a major merger, working with a team of over 60 frontend engineers to serve a platform with 10M+ merchants. As the founder of Websoul, he built a marketing automation and fintech SaaS from scratch, scaling it to serve 32,000+ businesses using Vue, TypeScript, and Supabase. This background in building and scaling data-intensive applications directly informs his AI work, where data pipelines, performance, and user experience are paramount.
Further experience includes staff-level engineering at Croissant, a workplace platform, where he partnered closely with the CEO and CTO on compliance and collaboration features. Earlier roles involved industrial ERP systems at Insoft Serwis and technical writing, which honed his ability to communicate complex technical concepts clearly. These diverse experiences across B2B SaaS, industrial tech, and large-scale platforms provide a robust context for implementing AI solutions that are both technically sound and business-relevant.
The Benefits of Hiring a Remote AI Engineer from Poland
Hiring a remote AI engineer based in Poland offers distinct advantages for international teams, particularly those in the EU, UK, and US. Poland's tech talent pool is renowned for its high level of education, strong work ethic, and proficiency in English. Engineers like Tom are accustomed to the cultural and communication nuances of working with Western European and North American companies, ensuring smooth collaboration.
From a practical standpoint, Poland's Central European Time zone provides significant overlap with both European and US East Coast business hours. This facilitates real-time meetings, pair programming sessions, and urgent collaboration when needed, while still allowing for productive deep work during asynchronous periods. The remote model also offers cost-efficiency without compromising on the quality or seniority of the engineering talent you access.
Tom's specific mode of operation includes availability for occasional on-site workshops, client visits, and team offsites, bridging the gap between fully remote and hybrid collaboration. This flexibility ensures that critical planning phases, kick-offs, or strategic reviews can benefit from in-person interaction when it adds the most value, followed by sustained remote execution. For companies building AI products, this blend of remote efficiency and targeted in-person collaboration is ideal.
AI Software Factory and Development Process
An AI Software Factory represents a systematic approach to building AI-powered products, emphasizing automation, quality gates, and reproducible processes. Tom helps teams establish these factories, which integrate AI coding agents, automated testing, and continuous integration/deployment pipelines tailored for AI workloads. This methodology moves beyond ad-hoc AI experiments to create a scalable, maintainable production environment for AI features.
The setup involves several key components. First, implementing agentic engineering workflows where AI assistants are leveraged for code generation, review, and testing. Second, establishing robust data and model versioning pipelines. Third, creating automated quality gates that evaluate not just code correctness but also model performance, bias, and drift. This structured approach significantly reduces time-to-market and increases the reliability of AI deployments.
For engineering teams, adopting an AI Software Factory led by an experienced remote AI engineer leads to dramatic improvements in developer experience and productivity. Tom focuses on upskilling teams, integrating tools like AG Grid for data-intensive interfaces, and ensuring the entire stack from backend APIs to frontend UX is optimized for AI interactions. The result is a team that is more confident, efficient, and capable of iterating on AI features rapidly and sustainably.
Technical Leadership and AI Strategy
Beyond hands-on coding, a senior remote AI engineer provides essential technical leadership and strategic direction. Tom operates effectively in roles such as Staff Software Engineer, Founding Engineer, Tech Lead, or Fractional CTO, guiding the technical vision for AI initiatives. This involves making critical decisions on technology stack, architecture patterns, and build-vs-buy evaluations for AI components, always balancing innovation with practicality and cost.
His leadership style is collaborative and knowledge-sharing. He has authored technical books, spoken at conferences, and conducts corporate training on AI topics. This makes him particularly effective at mentoring team members, conducting architecture reviews, and facilitating workshops for executives on AI strategy. For founders and CTOs, this provides not just an execution partner but a thought leader who can help navigate the rapidly evolving AI landscape.
This strategic capability is crucial for aligning AI projects with business objectives. Tom helps define key metrics for AI success, establish ethical guidelines for AI use, and create roadmaps that deliver incremental value. Whether advising on an MVP architecture for a startup or modernizing a legacy system with AI capabilities for an enterprise like GE Healthcare or Unilever, his focus is on creating tangible business outcomes through technology.
When This is a Good Fit
Engaging Tom as your remote AI engineer is an excellent fit if you are a founder, CTO, or executive leading a team in the EU, UK, or US. It is ideal if you need a product-minded engineer who can take ownership of AI projects from concept to production, modernize existing systems with AI capabilities, or establish an AI software factory to boost team productivity. This collaboration works best for organizations that value clear communication, strategic technical leadership, and a hands-on approach to building scalable, user-focused AI products.