Python Developer AI ML

Location: Gliwice, Hybrid - One day a week from the office (Thursday) Salary: 25.000 PLN brutto, 75% PKUP - UoP About Us: Join our dynamic co.brick Observe team, where you'll have the opportunity to co-create and shape the development direction of the innovative AI-driven platform. Our mission is to create a seamless, AI-powered ecosystem that monitors systems in real-time, prevents failures, reduces downtime, and delivers fully autonomous 24/7/365 support. You will drive Proof of Concepts (PoCs) and MVPs from the ground up in a highly collaborative, fast-paced environment. The work you do here will define the next generation of AI-powered user interactions. We're looking for someone with a deep passion for innovation who excels in the agile, high-energy atmosphere of a startup. If you're ready to build what's next, let's talk. Key Responsibilities: Design and implement scalable backend services in Python that support, ML and generative AI solutions, for knowledge inference and user support. Develop and maintain semantic knowledge bases using Retrieval-Augmented Generation (RAG) processes to enhance the system's ability to provide accurate, contextually relevant information and support. Continuously improve data inference models to enable better predictions, and automated recommendations, contributing to the strategic development of the system's functionalities. Collaborate with cross-functional teams to ensure the seamless integration of generative AI and semantic knowledge solutions into the broader system, improving overall performance and user experience. Development of AI/ML Proof of Concepts (PoCs) and Minimum Viable Products (MVPs), showcasing innovative solutions and demonstrating the potential of new technologies in real-world applications. Collaborating with other team members on best practices, code quality, and new AI technologies. Stay informed about emerging AI technologies, trends, and best practices. Apply this knowledge to improve data engineering processes and techniques continuously. What You'll Bring: 3 years of Python development experience, with proven experience in driving projects from concept to deployment. Strong hands-on experience in generative AI/NLP solutions and Machine Learning model development and deployment. Ability to argue for technical decisions and influence the product's development direction. Familiarity with semantic knowledge bases and Retrieval-Augmented Generation (RAG) processes, along with experience in integrating AI solutions for knowledge inference and support systems. Experience working in cross-functional teams, collaborating with data scientists and engineers to ensure smooth integration of AI solutions into complex systems. Strong knowledge of conversational, LLM-based AI agents, and multiagent systems. Excellent communication skills, allowing for the clear presentation of complex technical concepts to both technical and non-technical audiences. Preferred Qualifications: Strong experience with Python frameworks (e.g. FastAPI) including designing RESTful APIs, implementing asynchronous endpoints, and building scalable applications following best practices. Experience with version control systems (e.g. Git). Knowledge of various database systems (e.g. PostgreSQL, Redis) Experience with setting up and managing vector data bases (e.g. Qdrant, PGVector) Proficiency with LLM-specific models, prompt engineering, ReAct agents (Reasoning and Acting), libraries, and frameworks, such as Hugging Face, LlamaCloud, or LLMs APIs (eg. OpenAI API), Experience with fine-tuning large language models for specialized tasks. Experience with MLOps tools relevant to generative AI, such as LangChain, LlamaIndex, prompt engineering, and scaling LLM deployments. Strong understanding of retrieval-augmented generation (RAG) systems Strong analytical and problem-solving skills Excellent communication and teamwork abilities to collaborate effectively with cross-functional teams. Nice to have: Experience and knowledge of AI/ML models for anomaly detection, prediction and similar tasks in observability systems with the ability to leverage these models to enhance system reliability and proactively resolve issues. Experience with prototyping frameworks such as Streamlit to enable rapid development and deployment of user interfaces for prototype applications and functionalities. Familiarity with Model Context Protocol (MCP)