Python Developer – Senior
SOFTSWISS continues to expand its AI capabilities, and we are looking for a Senior Python Developer to join our dedicated AI Software Delivery team. We need a true, experienced, and accomplished professional who is passionate about the intersection of LLMs, software engineering, and scalable infrastructure.
You will design and build sophisticated AI-enabled solutions, including Retrieval-Augmented Generation (RAG) systems, autonomous agents, and Model Context Protocol (MCP) integrations. You will help shape the architecture of our AI ecosystem, ensuring that our internal tools are not just “smart,” but also robust, scalable, and deeply integrated with our enterprise data.
Your deep understanding of Python internals, distributed systems, and modern AI orchestration patterns will drive the reliability and performance of our next generation of software.
- Collaborating within an agile team using Scrum methodology and participating in the strategic planning of the AI roadmap
- Engaging in architectural, infrastructure, and strategic decision-making processes for AI-enabled services
- Designing and implementing production-ready RAG pipelines and Agentic workflows that leverage our internal data
- Integrating Model Context Protocols (MCP) to standardize how our AI tools interact with diverse data sources and environments
- Conducting code reviews for other team members and maintaining high standards for documentation and testability
- 7+ years of professional software development experience
- English – Intermediate or higher
- At least 4 years of recent programming experience with Python as the primary language
- Strong understanding of Python internals: asynchronous programming (asyncio), memory management, and performance profiling
- Proven experience building RAG (Retrieval-Augmented Generation) systems and working with LLM orchestration frameworks (e.g., LangChain, LlamaIndex, or CrewAI)
- Solid knowledge of Linux, and proficient command-line skills (Bash/Shell)
- Solid knowledge of Docker and Kubernetes for deploying and scaling containerized applications
- Experience with Relational Databases (PostgreSQL), including query optimization and schema design
- Experience with Redis for caching and state management in distributed systems
- Familiarity with the ELK Stack (Elasticsearch, Logstash, Kibana) for logging, monitoring, and observability
- Experience with Vector Databases (e.g., pgvector, Milvus, Qdrant) and semantic search optimization
- Experience implementing Model Context Protocol (MCP) or similar interoperability standards.
- Understanding of distributed application principles and microservice architecture
- Familiarity with modern back-end design patterns
- Experience in fields where data security and integrity are paramount
