Aisera - Tech Lead/Architect- Python

Remote - Athens, Greece1mo ago

Remote Principal EMEA Cloud Computing Artificial Intelligence Tech Lead Docker Kubernetes Python Vector Kafka

Requirements

• At least 7 years in senior software engineering roles, with 2+ years as tech lead or architect • 2+ recent years professional work on AI or Search systems • Expertise in vector search, embedding models, or RAG system optimization • Strong background in distributed systems, APIs, and cloud-native AI infrastructure • Experience with AI frameworks (LangChain, LlamaIndex, etc.), and vector databases or information retrieval systems (preferably Elastic) • Solid understanding of software development principles and architectural best practices. • Professional experience with messaging systems such as Kafka, RabbitMQ, or similar technologies. • Professional experience with cloud platforms such as AWS, GCP or Azure. • Proficiency in using Docker, Kubernetes, or similar containerization and orchestration tools. • Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field. • Ph.D. in distributed systems, AI systems, or related area • Familiarity with machine learning concepts and libraries (e.g., Pandas, NumPy, PyTorch). • Experience with LLM integration, prompt engineering, and AI orchestration frameworks. • This is a Greece-based, full-time, employee-status, fully remote position.

Responsibilities

• Architect and build scalable agentic AI systems capable of autonomous decision-making and task execution • Architect, design and optimize dialog agents with natural language understanding, context management, and multi-turn conversation capabilities • Develop and optimize robust search and retrieval systems for RAG implementations, including vector databases, embedding strategies, and knowledge integration • Take ownership of complex system design, from proof-of-concept to production deployment • Establish best practices for AI model integration, prompt engineering, and system reliability • Collaborate with product teams to translate business requirements into technical AI solutions • Develop and maintain scalable, efficient data pipelines that automate the ingestion, transformation, and storage of data • Identify performance bottlenecks and improve systems scalability.