Senior AI Engineer

Tooploox is part of Solvd Inc., operating in Poland with a focus on meaningful AI projects and global reach. The company emphasizes a creative, human-scale culture and works on production-grade AI agents.

• Advanced expertise in Python, focusing on clean, modular code and production-ready applications.
• Solid software engineering knowledge including system design, database management, and deep understanding of REST API principles.
• Readiness to lead client collaboration with clear communication.
• Deep practical experience with modern LLM orchestration and state-management frameworks.
• Strong understanding of Retrieval-Augmented Generation (RAG) including hybrid search, re-ranking, graph-based retrieval, and proficiency in vector databases.
• Experience implementing tracing, monitoring, and evaluation for complex agent flows.
• Experience with API/SDKs of major model providers and Model Context Protocol (MCP).
• Experience configuring services on cloud platforms (AWS, Azure, GCP) and containerization (Docker/Kubernetes).
• Proficient in automating CI/CD processes and understanding DevOps practices.
• Fluency in English for client meetings.

Nice to have:

• TypeScript experience.
• Knowledge of machine learning principles or model fine-tuning.
• Experience running and serving local LLMs (e.g., via Ollama, vLLM, or TGI).
• Familiarity with Voice Agents concepts and frameworks like Pipecat or LiveKit.
• Experience with graph databases (e.g., Neo4j) or GraphRAG approaches.

• Collaborate with and lead cross-functional teams to design and build stateful, multi-agent workflows using modern orchestration patterns.
• Engage with clients to define requirements and oversee the full software development lifecycle, from proof of concept through evaluation and implementation to production.
• Implement rigorous evaluation pipelines (e.g., LLM-as-a-Judge) to quantitatively measure agent performance and reliability before deployment.
• Proactively drive productivity and adapt to evolving demands, taking initiative consistently.
• Translate stakeholders’ needs into requirements and drive backend (and sometimes frontend) development to ship functional end-to-end prototypes.
• Stay informed about rapid advancements in the field, continuously learning and integrating new knowledge.
• Employ a pragmatic, evaluation-driven approach to adopting innovative AI solutions.