Tom Smykowski is a product-minded LLM engineer contractor with nearly two decades of experience delivering software from discovery through production. He specializes in AI-native development, LLM integration, and building AI software factories, making him an ideal hire for founders, CTOs, and executives seeking to augment their teams with senior-level, hands-on AI engineering expertise. Based in Poland, he works remotely with international teams across the EU, UK, and US, providing the technical leadership and execution needed to turn complex AI requirements into scalable, user-focused products.
Why Hire an LLM Engineer Contractor?
Bringing on a specialized LLM engineer as a contractor offers a strategic advantage for organizations navigating the rapid evolution of artificial intelligence. It provides immediate access to deep technical expertise without the long-term commitment and overhead of a full-time hire. This model is particularly effective for projects requiring specific AI integration, such as implementing agentic workflows, building custom AI coding assistants, or modernizing existing systems with LLM capabilities.
For teams looking to accelerate their AI initiatives, a contractor like Tom can hit the ground running. His approach combines hands-on engineering with product thinking and technical leadership. He has a proven track record of ownership, having worked with clients like Croissant, Printify, and GE Healthcare, where he delivered compliance-focused features, large-scale design systems, and industrial SaaS solutions. This background ensures that AI projects are not just technically sound but also aligned with business goals and user needs.
Engaging an LLM engineer contractor is especially valuable for validating AI product concepts, scaling MVP prototypes, or upskilling an existing engineering team in AI-native development practices. It allows companies to de-risk their AI investments by leveraging proven experience while maintaining flexibility. Tom's remote work setup from Poland facilitates seamless collaboration with teams in European, UK, and US East Coast time zones, ensuring progress continues without geographical constraints.
Core LLM Engineering and AI Integration Expertise
Tom's expertise spans the full spectrum of applied AI engineering, with a focus on practical, production-ready implementations. His work involves integrating large language models into existing software ecosystems, developing AI-assisted workflows, and setting up the infrastructure necessary for reliable AI applications. This goes beyond simple API calls, encompassing prompt engineering, retrieval-augmented generation (RAG) systems, and the development of agentic frameworks that can autonomously execute complex tasks.
A significant part of his value lies in establishing AI software factories. This involves configuring AI coding agents like Cursor or GitHub Copilot, implementing AI-native software development lifecycles, and setting up quality gates and automated testing specifically for AI-generated code. By optimizing these processes, he helps engineering teams dramatically increase developer productivity and code quality, reducing time-to-market for new features and products.
His technical foundation in modern web stacks, including Vue.js, TypeScript, and Supabase, allows him to seamlessly weave AI capabilities into both frontend and backend systems. Whether the goal is to build an intelligent chatbot for a customer service platform, automate content generation for a marketing SaaS, or create a sophisticated data analysis tool using AG Grid enhanced with AI insights, Tom applies a full-stack, product-oriented mindset to ensure the solution is robust, maintainable, and delivers tangible user value.
Staff-Level Engineering for AI Projects
Tom operates at a staff or principal engineer level, which means he brings strategic technical leadership alongside his implementation skills. For AI projects, this is critical. It involves making foundational architecture decisions that affect scalability, cost, and maintainability. He evaluates when to use off-the-shelf LLM APIs versus fine-tuning open-source models, designs data pipelines for training and inference, and establishes monitoring and evaluation frameworks to track AI performance in production.
His experience at Croissant as a Staff Software Engineer, where he partnered closely with the CEO and CTO on compliance and collaboration features, demonstrates his ability to handle high-stakes, complex product domains. This level of responsibility translates directly to AI initiatives, where ethical considerations, data privacy, and system reliability are paramount. He is adept at navigating the trade-offs between innovation speed and technical debt, ensuring that AI integrations support long-term business growth.
This seniority also enables him to effectively mentor and upskill in-house teams. As AI becomes a core competency, having a contractor who can transfer knowledge and establish best practices is invaluable. Tom has a strong background in technical writing, public speaking, and workshops, which he leverages to conduct corporate AI training, executive briefings, and engineering team upskilling sessions. This ensures that the organization builds internal capability alongside the delivered project.
Remote Collaboration and International Team Fit
As a remote contractor based in Poland, Tom is expertly set up for distributed collaboration. He has extensive experience working with teams across the EU, UK, and US, including maintaining productive overlap with US East Coast time zones. His communication style is tailored for async-first environments, utilizing clear documentation, structured updates, and proactive stakeholder management to keep projects moving forward smoothly, regardless of location.
This remote model offers significant flexibility for companies. It allows access to top-tier AI engineering talent without relocation costs or visa complexities. Tom is available for occasional on-site workshops, client visits, and team offsites, providing the benefits of in-person collaboration when strategically valuable. This hybrid approach combines the efficiency of remote work with the relationship-building and intensive problem-solving sessions that sometimes require face-to-face interaction.
His client list, which includes organizations like DeLaval, Ordnance Survey, Unilever, and the Silesian University of Technology, showcases his ability to adapt to different industry contexts and team cultures. Whether integrating AI into agricultural tech, geospatial systems, consumer goods logistics, or academic research, he focuses on understanding the domain-specific problems and collaborating effectively with subject matter experts, product managers, and fellow engineers to craft the right solution.
From AI Strategy to Deployed Product
Tom's engagement model is designed to take AI projects from initial concept to live deployment. He begins with discovery and strategy, which can include AI software factory consultations, architecture audits, or technical due diligence on existing AI approaches. This phase helps clarify objectives, assess technical feasibility, and define a roadmap, whether the goal is to build an MVP, modernize a system with AI features, or scale an existing SaaS platform.
The execution phase involves hands-on development, where he builds, integrates, and iterates. His work on the Printify design system rebrand, which supported a platform serving 10 million merchants and approximately 60 frontend engineers, demonstrates his capacity for projects at scale. For AI, this translates to building systems that are not only functional but also performant, secure, and capable of handling real-world load. He emphasizes creating maintainable codebases and clear APIs, ensuring that the AI components can evolve alongside the rest of the application.
Finally, he ensures a smooth path to production and beyond. This includes setting up CI/CD pipelines adapted for AI models, implementing monitoring for drift and performance, and planning for iterative improvement based on user feedback and data. His product-minded approach means he remains focused on the end-user experience and business outcomes throughout the development cycle, ensuring the AI delivers measurable value.
When This is a Good Fit
Engaging Tom as your LLM engineer contractor is an excellent fit if you are a founder, CTO, or engineering leader needing staff-level AI expertise on a flexible basis. It is ideal for projects requiring deep technical ownership, from architecture to production, such as building an AI-powered SaaS feature, implementing an agentic workflow, or setting up an AI software factory to boost team productivity. This model suits remote-first or distributed teams in the EU, UK, or US seeking a seasoned professional who can integrate seamlessly, communicate effectively across time zones, and deliver high-impact results without the overhead of a permanent hire.