Tech Lead/Architect - Python (AI/Search Systems)
A
AiseraAI Copilot solutions
GreeceFull-TimeLead
Salary not disclosed
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Job Details
- Experience
- At least 7 years in senior software engineering roles, with 2+ years as tech lead or architect
- Required Skills
- AWSDockerPythonElasticSearchGCPKafkaKubernetesRabbitmqAzureRESTful APIsLangChainDistributed Systems
Requirements
- At least 7 years in senior software engineering roles
- 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.)
- Experience with 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
Responsibilities
- Lead the design and development of advanced AI systems including intelligent agents, conversational interfaces, and retrieval-augmented generation (RAG) architectures and systems
- Drive technical strategy and implementation for next-generation AI products
- 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
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