AI / LLM Software Engineer

Blau Direkt Poland is hiring an experienced AI / LLM Software Engineer with a strong backend focus in Golang to build production-ready AI capabilities. The role involves designing and building reliable systems around LLMs including RAG pipelines, AI agents, model integrations, observability, cost control, and AI-specific security.

• Several years of experience in software engineering, ideally with a strong backend focus.
• Practical experience with LLM-based applications, AI workflows, or AI-assisted products.
• Good understanding of RAG concepts: vector databases, embedding models, chunking, retrieval quality, and hybrid search.
• Experience integrating LLM APIs, working with prompts, structured outputs, and tool/function calling.
• Awareness of AI-specific security risks, including prompt injection, PII stripping, data leakage, and unsafe tool execution.
• Strong backend development experience, preferably with Go.
• Solid understanding of software architecture, clean code, and system design.
• Experience with Docker, Git, CI/CD, and production environments.
• Ability to use modern AI development tools such as Cursor, Claude, Gemini, or JetBrains AI effectively.
• Strong ownership, pragmatic mindset, and good communication skills.
• Fluent English, minimum B2.

Nice to have:

• Experience with vector databases such as Qdrant, Pinecone, Weaviate, Milvus, Elasticsearch, or OpenSearch.
• Experience with AI evaluation, retrieval quality testing, or automated testing of non-deterministic systems.
• Experience with event-driven architectures and asynchronous processing.
• Experience with Kubernetes, RabbitMQ, MySQL, or MariaDB.
• Experience with React and TypeScript.
• Knowledge of PHP or legacy system integration.
• Knowledge of German.

• Design and build production-ready AI / LLM-based features and platform components.
• Integrate LLM APIs and AI models such as OpenAI, Anthropic, Gemini, or local models.
• Build and optimize RAG pipelines, including vector databases, embeddings, chunking strategies, and hybrid search.
• Design and implement AI agents and agentic workflows using LLMs, tools, APIs, and internal data sources.
• Develop backend services and APIs, primarily in Go and occasionally in PHP.
• Monitor and optimize LLM costs, token usage, latency, reliability, and output quality.
• Implement safeguards against prompt injection, data leakage, unsafe tool usage, and improper PII handling.
• Design maintainable systems using clean architecture, DDD, and pragmatic engineering principles.
• Evaluate new AI technologies and apply them where they provide real business value.
• Collaborate with product and engineering teams to bring AI concepts from prototype to production.