Responsibilities
- Write high-quality, maintainable Python code, adhering to established coding standards and best practices.
- Conduct thorough code reviews, effective debugging, and comprehensive testing to ensure software reliability and performance.
- Implement and maintain CI/CD pipelines using automation tools such as GitHub Actions, Azure DevOps, Docker, and Kubernetes, streamlining deployment workflows.
- Develop, containerize, and deploy AI solutions across enterprise environments (Dev, QA, Stage, Prod).
- Integrate logging, monitoring, and alerting systems to enable operational reliability and proactive issue resolution.
- Collaborate with cross-functional teams to align technical solutions with business goals and performance standards.
- Design and implement AI agentic solutions using frameworks such as LangChain, LangGraph, LangFlow, AutoGen, or similar.
- Customize and optimize LLM-powered architectures for scalability, reliability, and real-world applicability.
- Evaluate new GenAI frameworks, tools, and methods to continuously improve solution performance and developer productivity.
- Integrate external APIs, vector databases, and retrieval-augmented generation (RAG) pipelines into production-ready systems.
Basic qualifications
- 5+ years of professional software engineering experience, with advanced proficiency in Python.
- Strong understanding of containerization (Docker) and orchestration (Kubernetes).
- Proven experience building and maintaining CI/CD pipelines (GitHub, Azure DevOps, Jenkins, or equivalent).
- Experience deploying applications in cloud environments (Azure, AWS, or GCP).
- Familiarity with monitoring tools (Prometheus, Grafana, ELK stack) and best practices in observability.
- Practical hands-on experience developing or extending LLM-based applications using agentic frameworks (LangChain, LangGraph, etc.).
- Strong problem-solving, communication, and documentation skills.
Preferred qualifications
- Experience with vector databases (FAISS, Pinecone, Weaviate, Chroma).
- Familiarity with RAG pipelines, prompt engineering, or model orchestration.
- Exposure to MLOps, AI evaluation pipelines, and cloud-based AI deployments.
- Understanding of security, authentication, and data privacy best practices in AI systems.
- Attractive salary
- Large freedom and real influence
- No unhealthy competition, team approach to meeting challenges
- Remote-first, flexible working culture
- Company apartments in cool cities across Europe: work and enjoy a memorable getaway
About the company
- Python: 5 years (Required)
- AI: 4 years (Required)
Tags & focus areas
Used for matching and alerts on DevFound Fulltime Remote Ai Ai Engineer