Who is Tom Smykowski, Your LLM Integration Engineer?
Tom Smykowski is a remote LLM integration engineer based in Poland, specializing in embedding Large Language Models into production software systems for EU, UK, and US teams. With nearly two decades of experience in product delivery, combining engineering, UX, and product thinking, he is the ideal hire for technical leaders who need ownership from discovery through production. He excels at turning complex AI requirements into maintainable, scalable, and user-focused products, making him a strategic partner for founders, CTOs, and executives aiming to leverage AI competitively.
His work is not just about API calls. It encompasses the entire AI-native software development lifecycle, from strategic planning and model selection to integration architecture, prompt engineering, evaluation, and scaling. This holistic approach ensures that LLM integrations are robust, cost-effective, and aligned with business goals. Tom’s background includes significant tenures as a Staff Software Engineer at Croissant, where he partnered closely with the CEO and CTO on compliance and collaboration features, and leading frontend and design system work for Printify, supporting over 10 million merchants and a team of around 60 frontend engineers.
Tom’s approach is fundamentally product-minded. He understands that successful LLM integration is not a purely technical challenge. It requires a deep empathy for end-users, a clear vision of the product roadmap, and an ability to communicate complex AI concepts to stakeholders across the organization. This blend of skills makes him uniquely suited to bridge the gap between ambitious AI strategy and tangible, shipped features that deliver real value.
Defining the LLM Integration Engineering Role
An LLM integration engineer is more than a developer who works with AI APIs. This role is a specialized discipline focused on the end-to-end process of incorporating generative AI capabilities into existing or new software products. It involves selecting the right models and providers, designing secure and efficient API communication patterns, implementing robust orchestration layers, and establishing monitoring and evaluation frameworks. The engineer must ensure the integration is performant, reliable, and economically sustainable at scale.
Key responsibilities include architecting the data flow between the application and LLM services, implementing caching and retry logic, managing context windows intelligently, and handling streaming responses for a seamless user experience. Furthermore, an LLM integration engineer must consider ethical AI use, bias mitigation, and compliance with data privacy regulations like GDPR, especially when dealing with user data. This requires a nuanced understanding of both the technological landscape and the regulatory environment.
Ultimately, the goal is to move beyond proof-of-concept demos to production-grade systems. This demands engineering rigor around testing, versioning of prompts and model configurations, A/B testing of different approaches, and setting up observability to track costs, latency, and quality metrics. An LLM integration engineer like Tom brings this systematic, engineering-first mindset to AI projects, ensuring they are built to last and evolve.
Technical Expertise for Enterprise AI Integration
Tom’s technical foundation is built on modern full-stack development, which is critical for seamless LLM integration. His deep expertise with TypeScript, Vue.js, and React provides the strong frontend capabilities necessary for building intuitive AI-powered interfaces. On the backend, his experience with Supabase and various APIs allows him to design the data pipelines and serverless functions that power AI features. This full-stack proficiency ensures he can own the integration from the UI component all the way down to the infrastructure logic.
His project portfolio demonstrates a consistent ability to handle complexity at scale. At Printify, he led the design system and rebranding effort ahead of a major merger, an undertaking that required meticulous planning, coordination across large engineering teams, and a focus on developer experience. Similarly, as the founder of Websoul, he built a marketing automation platform serving over 32,000 businesses, which involved complex state management, data grid implementations with AG Grid, and building a reliable fintech SaaS. This experience translates directly to the challenges of scaling LLM integrations, where system reliability, developer tooling, and maintainability are paramount.
Beyond core web technologies, Tom’s work encompasses AI-assisted workflows and tooling. He has practical experience with AI coding agents, Claude Code, and GitHub Copilot, understanding their impact on developer productivity and software quality. This hands-on knowledge of the AI-Native SDLC (Software Development Lifecycle) allows him to not only integrate LLMs for end-users but also to advise on and implement AI tooling within engineering teams themselves, creating a compounding effect on innovation and efficiency.
From Strategy to Production: The AI Integration Process
The journey from an AI idea to a live feature follows a structured process that Tom expertly navigates. It begins with a discovery phase, aligning the business objective with feasible AI capabilities. This involves evaluating use cases, assessing data availability, and selecting the most appropriate models (e.g., OpenAI GPT, Anthropic Claude, open-source alternatives) based on cost, performance, and privacy requirements. Tom leverages his product thinking to ensure the proposed integration delivers clear user value and aligns with the product roadmap.
Next comes the architectural design phase. Here, Tom plans the integration pattern. Will it be a simple chat interface, a complex agentic workflow with tool calling, or a background processing engine for content generation? He designs the system components, data flows, and APIs, paying special attention to error handling, rate limiting, and security. His experience with enterprise clients like DeLaval, GE Healthcare, and Unilever means he is adept at building systems that meet high standards for reliability and compliance, crucial for regulated industries.
The final phase is implementation, testing, and deployment. Tom leads or contributes hands-on to the development, employing best practices in code quality, testing (including specific evaluations for LLM outputs), and CI/CD. He establishes monitoring for key metrics like token usage, response latency, and user satisfaction. His goal is to ship a v1 that works reliably and is instrumented for learning, creating a foundation for iterative improvement based on real-world usage data and feedback.
Client Success and Cross-Industry Application
Tom’s ability to deliver successful integrations is evidenced by his work with a diverse range of clients, from global corporations to innovative startups. His tenure as a Staff Software Engineer at Croissant involved building features that required a deep understanding of user collaboration and platform compliance. For industrial clients like DeLaval and through his role at Fream, he worked on complex SaaS solutions involving design systems, interactive maps, and conversion-focused UX. These projects often involved integrating sophisticated logic and data visualization, skills that are directly transferable to creating clear and actionable interfaces for AI outputs.
His contributions to public sector and institutional projects, such as those for the Polish Tourism Organisation, the Institute of National Remembrance, and several universities, demonstrate an ability to navigate unique constraints and stakeholder needs. These projects often involve legacy systems, specific accessibility requirements, and complex data governance, all of which are highly relevant when integrating AI into established enterprise environments. The common thread is a focus on solving real problems with technology, not just implementing technology for its own sake.
This cross-industry experience is a significant asset for an LLM integration engineer. It means Tom can quickly understand the domain-specific challenges of a new client, whether in healthcare, agriculture, e-commerce, or education. He can anticipate integration pitfalls, communicate effectively with both technical and non-technical stakeholders, and design solutions that are appropriate for the specific business context, ensuring the AI integration drives meaningful outcomes.
Remote Collaboration and Time-Zone Alignment
Operating remotely from Poland, Tom is expertly set up for productive collaboration with international teams. He is experienced in the async communication, documentation, and project management practices that distributed work requires. His work style is built for remote-first and remote-friendly environments, ensuring seamless integration into your existing processes. For teams that value occasional in-person connection, he is available for on-site workshops, client visits, and team offsites, blending the efficiency of remote work with the high-bandwidth benefits of face-to-face meetings when needed.
Time-zone overlap is a key consideration for real-time collaboration. Based in Poland, he naturally aligns with European Union and United Kingdom working hours. Furthermore, he is accustomed to and available for overlap with US East Coast teams, facilitating morning stand-ups, planning sessions, and collaborative problem-solving windows. This flexibility ensures that he can be a synchronous partner for key meetings while leveraging async work for deep focus time, a balance that maximizes productivity for both him and the teams he works with.
His engagement models are designed for flexibility. Whether you need a full-time staff engineer to lead your AI initiatives, a part-time expert to guide a specific integration project, or a consultant to audit your AI strategy, Tom can structure the engagement accordingly. He works on B2B contracts, as an independent contractor, and is open to full-time employment with the right opportunity. This adaptability allows organizations of all sizes and stages to access his level of expertise.
When This is a Good Fit
Hiring Tom as your LLM integration engineer is an excellent fit if you are a founder, CTO, or technical lead who needs more than just coding. You need a product-minded partner who can own the AI integration from concept to launch. It's ideal if you have a clear vision for using AI in your product but lack the in-house expertise to architect and execute the integration with confidence. It's also a strong fit if you have an existing integration that needs scaling, optimization, or modernization to improve reliability and cost-efficiency. If you value clear communication, strategic thinking, and a focus on delivering tangible business value through technology, then this collaboration will be highly productive.