Throughout our recruitment services, we act as trusted advisors – supporting and guiding candidates beyond the hiring process to help them make the best possible decision for their career and future. Our goal is not just to place talent in new positions, but to help them take meaningful steps forward. Every candidate receives personalized attention – a value reflected in our award-winning, people-focused approach. With SWICON, you’re in good hands – especially when it matters most.
Introduction
Our partner has the AI Lab inside a Hungarian fintech that powers POS terminals and digital banking solutions. While others talk about AI, we ship it. Our systems process real transactions, handle real customer conversations, and make real decisions. Now we need engineers who can build both the brain and the body of these products.
Tasks
On the AI Side:
- Design and build production RAG pipelines—chunking strategies, embedding models, vector databases (Pinecone, Weaviate, ChromaDB), and retrieval optimization
- Architect multi-agent systems using LangGraph and LlamaIndex for complex financial workflows
- Fine-tune open-source LLMs (Llama, Qwen, Mistral) for domain-specific tasks when API calls aren't enough
- Build AI microservices in Python/FastAPI that serve models with sub-second latency
- Implement evaluation frameworks (DSPy) to measure and improve model performance systematically
On the Product Side:
- Build responsive frontends in React (Next.js) and TypeScript that make AI features intuitive
- Develop backend services in Node.js/NestJS or Python that orchestrate AI capabilities with business logic
- Own the full lifecycle: you build it, you deploy it, you monitor it, you improve it
- Work directly with product managers to translate business problems into technical solutions
Projects You'll Touch:
- Call Center Intelligence: Whisper-based transcription with LLM post-processing, task extraction, and Teams Bot integration
- Document Understanding: RAG pipelines with DSPy optimization for financial document Q&A
- Multi-Agent Workflows: Autonomous agents that handle complex, multi-step financial processes
- AI-Powered Internal Tools: Chat interfaces (Chainlit, custom React) that boost team productivity
Tech Stack:
- Cloud: Azure (AKS, Azure OpenAI Service), Docker, Terraform
- AI/ML: PyTorch, Hugging Face, LangChain, LlamaIndex, DSPy, Whisper
- Backend: Python (FastAPI), Node.js/TypeScript (NestJS)
- Frontend: React (Next.js), TypeScript
- Data: PostgreSQL, Vector DBs (Pinecone, Weaviate, ChromaDB)
- Observability: Prometheus, Grafana, Loki
Expectations
- 4+ years of production software engineering experience
- Strong Python skills (you can write clean, testable, production-ready code)
- Hands-on experience with at least one GenAI stack (LangChain, LlamaIndex, or similar)
- You've built and deployed web applications—frontend or backend, ideally both
- You can explain complex technical concepts to non-technical stakeholders
Advantages
- Experience with RAG systems in production (not just tutorials)
- You've fine-tuned LLMs for real use cases
- Background in fintech, banking, or high-stakes production systems
- MLOps experience (model monitoring, A/B testing, deployment pipelines)
- Contributions to open-source AI projects
Employer's offer
- Real Impact: Your code affects millions of transactions. This isn't a side project—it's core product.
- Ownership: Small team, big responsibility. No waiting months for approvals. Ship fast, learn faster.
- Modern Stack: We don't maintain legacy COBOL. You'll work with tools that didn't exist two years ago.
- No BS Culture: Flat hierarchy. Your ideas matter more than your title. We debate, decide, and move on.
- Budapest-Based Flexibility: Hybrid with actual flexibility. Come to the office when collaboration matters.
Tags