Who is a RAG Application Developer?
Tom Smykowski is a senior product engineer with nearly two decades of experience in software delivery, specializing in the architecture and implementation of Retrieval-Augmented Generation (RAG) applications. Hiring him as your RAG Application Developer means engaging a professional who combines deep technical expertise in AI integration with a strong product mindset, capable of owning projects from initial discovery and system design through to production deployment and iteration. His background is ideal for founders, CTOs, and executives seeking a staff or senior-level engineer to build robust, user-focused AI solutions that leverage proprietary data.
RAG technology represents a pivotal advancement in generative AI, enabling large language models to access and reason over external knowledge bases, documents, and real-time data. This moves applications beyond general chat into powerful, context-aware tools for customer support, internal knowledge management, research, and automated reporting. A skilled RAG developer must not only implement the core retrieval and generation pipelines but also architect the surrounding data ingestion, embedding, vector search, and evaluation systems, all while ensuring scalability, security, and a seamless user experience.
Tom's approach is grounded in product thinking and ownership. He understands that a successful RAG application is more than a technical proof of concept. it is a product that must solve a real business need, be maintainable by a team, and deliver measurable value. His experience working with diverse international teams across Europe, the UK, and the US ensures he can navigate the complexities of remote collaboration, asynchronous communication, and aligning technical execution with business objectives.
Technical Expertise for RAG Implementation
Building a production-ready RAG system requires a multifaceted skill set spanning full-stack development, data engineering, and AI/ML integration. Tom brings a proven track record across these domains. His core technical stack includes modern JavaScript frameworks like Vue.js and TypeScript, backend and data layer technologies such as Supabase, and complex UI component libraries like AG Grid, which are often essential for presenting retrieved information in enterprise applications. This full-stack proficiency ensures he can build the entire application surface, from the data ingestion API and vector database layer to the responsive frontend interface where users interact with the AI.
The architecture of a RAG application involves several critical components: a document processing pipeline for chunking and cleaning data, an embedding model to convert text into vector representations, a high-performance vector database for similarity search, a carefully tuned LLM for generation, and an evaluation framework to monitor accuracy and relevance. Tom's experience in system design, evidenced by his work on platforms serving tens of thousands of users at Websoul and scaling frontend systems for 60+ engineers at Printify, provides the necessary foundation to make sound architectural decisions. He focuses on creating systems that are not only functional but also scalable, secure, and cost-effective to operate.
Furthermore, his work incorporates AI-assisted workflows and a focus on developer experience, which translates directly into building efficient RAG development pipelines. This includes implementing CI/CD for AI models, setting up automated testing for retrieval quality, and creating internal tools to manage knowledge base updates. By applying engineering rigor to the AI development lifecycle, he ensures that the RAG application remains reliable and adaptable as requirements evolve.
Product Delivery and Client Experience
Tom's value extends far beyond writing code. He has a history of taking ownership of entire product deliveries for notable organizations, ensuring that technical solutions directly address user and business goals. His portfolio includes work for marquee clients such as DeLaval, Croissant, Ordnance Survey, Printify, GE Healthcare, and Unilever. At Croissant, as a Staff Software Engineer, he partnered closely with the CEO and CTO on compliance and collaboration features, a scenario analogous to integrating sensitive corporate data into a secure RAG pipeline. This experience is crucial for applications handling confidential information.
His role in leading the Printify design system and rebrand ahead of a major merger involved coordinating a large group of over 60 frontend engineers. This demonstrates his capability in technical leadership and managing complex, cross-team dependencies. skills essential when a RAG application becomes a central piece of infrastructure used by multiple departments or product teams. Similarly, founding Websoul and scaling it to serve 32,000+ businesses required a deep understanding of SaaS product cycles, user onboarding, and automation, all relevant to deploying AI products that users can trust and rely on daily.
These experiences across industrial SaaS, e-commerce, and marketing automation mean he understands the domain-specific challenges that RAG aims to solve. Whether it's parsing technical manuals for an industrial ERP system, organizing geographic data as seen with Ordnance Survey, or managing large-scale product catalogs, Tom has the context to design RAG applications that fit seamlessly into existing business workflows and data ecosystems.
Remote Collaboration with International Teams
As a Poland-based remote engineer, Tom is expertly positioned to collaborate with teams across the European Union, the United Kingdom, and the United States. His working style is built for distributed environments, emphasizing clear asynchronous communication, structured knowledge sharing, and reliable delivery. For teams on US East Coast time zones, he provides consistent overlap for synchronous meetings, workshops, and pair programming sessions, which are invaluable during the intensive design and review phases of a RAG project.
His communication skills are a significant asset. With a background in technical writing and public speaking, he excels at documenting complex architectures, creating clear API specifications, and explaining AI concepts to stakeholders with varying technical backgrounds. This is particularly important for RAG projects, where non-technical product managers and executives need to understand the capabilities, limitations, and maintenance requirements of the system. He can facilitate workshops to align the team on the application's scope, lead training sessions on how to use and curate the knowledge base, and provide ongoing support through mentoring and community building within the engineering team.
This remote-first approach is flexible and accommodates various engagement models, from full-time B2B contracts to project-based consulting. He is available for occasional on-site workshops, client visits, and team offsites, which can be highly effective for kickstarting a new RAG initiative or conducting deep-dive architecture reviews. His ability to integrate smoothly into existing processes makes him a low-friction addition to any team looking to accelerate its AI capabilities.
From Concept to Production AI Application
The journey from a RAG concept to a live, production application involves several critical phases where Tom's experience proves vital. It begins with discovery and scoping, where he works with stakeholders to define the specific use cases, success metrics, and data sources. His product engineering mindset ensures the project focuses on solving a tangible problem rather than pursuing technology for its own sake. He then moves into architecture design, selecting the appropriate stack of embedding models, vector databases (e.g., Pinecone, Weaviate, pgvector), LLM providers, and application frameworks.
The implementation phase combines data engineering to build robust ingestion pipelines, backend development to create the retrieval and orchestration APIs, and frontend work to craft an intuitive user interface. Tom's full-stack expertise allows him to own this entire pipeline or collaborate effectively with specialists. Crucially, he emphasizes the operational aspects: implementing logging and monitoring for retrieval performance, setting up evaluation workflows to track hallucination rates and answer relevance, and designing feedback loops to continuously improve the system's knowledge base.
Finally, deployment and iteration require a focus on DevOps, security, and scalability. Drawing from his experience with high-traffic platforms, he ensures the application is deployed using modern CI/CD practices, is secure against injection attacks, and can scale with user demand. His end-to-end ownership means he remains engaged post-launch to monitor performance, iterate based on user feedback, and plan the next phases of feature development, ensuring the RAG application evolves as a core business asset.
AI-Native Development and Strategic Consulting
Beyond direct implementation, Tom offers strategic consulting to help organizations adopt an AI-native software development lifecycle (SDLC). This is particularly valuable for teams embarking on their first major RAG project. He can advise on setting up an AI software factory. incorporating AI coding agents like Claude Code or Cursor, establishing AI-assisted testing and quality gates, and automating parts of the development workflow. This consulting accelerates the team's overall capability and embeds AI thinking into the engineering culture.
His advisory services include technical due diligence for AI projects, architecture reviews of proposed RAG systems, and code audits focused on AI integration patterns. For leadership, he provides CEO/CTO AI briefings to help executives understand the strategic implications, costs, and opportunities of RAG technology. He also conducts corporate training and team workshops specifically on building and maintaining RAG applications, upskilling internal teams to become self-sufficient.
This holistic approach ensures that hiring Tom as a RAG Application Developer delivers not just a single application but also elevates the team's long-term competency in AI. He acts as a force multiplier, transferring knowledge and establishing best practices that remain within the organization. Whether engaged as a fractional technical lead, a project-based consultant, or a full-time staff engineer, his goal is to build a sustainable foundation for AI innovation.
When This is a Good Fit
Engaging Tom as your RAG Application Developer is an excellent fit if you are a founder, CTO, or product leader needing a senior engineer who can take full ownership of building a complex AI-powered application from the ground up. It is ideal for teams that value product-minded engineering, clear communication, and a focus on delivering user value over mere technical experimentation. His remote collaboration model works seamlessly for distributed teams in the EU, UK, and US seeking high-level expertise without the overhead of traditional enterprise consulting. If your project involves turning proprietary data into an intelligent, conversational interface or an automated knowledge system, and you need a reliable partner to navigate the full journey from concept to scalable production, then this collaboration is designed for your success.