Satalia
51 - 200 employees
Founded 2010
🤖 Artificial Intelligence
🤝 B2B
🏢 Enterprise
💰 Grant on 2016-02
Artificial Intelligence • B2B • Enterprise
Satalia is a company that leverages advanced technologies to optimize and improve various business operations. They provide a platform for cloud development and data-driven services, employing cloud architects and data scientists to deliver innovative solutions. Satalia operates remotely, with locations in Greece, London, Kaunas, and Vienna. They focus on integrating analytics and marketing strategies to enhance user experience and functionality on their platforms.
Data Scientist
🕒 January 28
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Satalia
51 - 200 employees
Founded 2010
🤖 Artificial Intelligence
🤝 B2B
🏢 Enterprise
💰 Grant on 2016-02
Artificial Intelligence • B2B • Enterprise
Satalia is a company that leverages advanced technologies to optimize and improve various business operations. They provide a platform for cloud development and data-driven services, employing cloud architects and data scientists to deliver innovative solutions. Satalia operates remotely, with locations in Greece, London, Kaunas, and Vienna. They focus on integrating analytics and marketing strategies to enhance user experience and functionality on their platforms.
📋 Description
• Design and deliver production-grade agentic systems, from multi-agent orchestration to the AI services and models that power them • Assess fit of agentic solutions per use case, being intentional in system design • Design the most suitable orchestration pattern per use case • Evaluate and recommend the best suited agent development framework • Engineer multi-step agent workflows that are reliable, auditable and modular, supporting both linear and non-linear control flows • Implement agent memory and state management systems • Optimise context assembly for each agent interaction • Evaluate and select the most appropriate foundation models, embeddings and context-specific model variants; apply fine-tuning or adaptation where needed • Implement guardrails to ensure operation within ethical, legal, brand boundaries • Build services for agent use, including ML models, GenAI and task-specific APIs • Process multi-modal data using appropriate embeddings and vector retrieval • Build, evaluate and maintain robust RAG and / or GraphRAG pipelines • Implement fallback strategies (e.g., retries, backup tools, escalation, safe exits) • Build and integrate MCP servers to expose and consume data and AI services • Use Google's A2A protocol to support independent agent communication, designing composable service boundaries and appropriate integration patterns • Containerise applications (Docker) – our platform team manages K8s • Apply AgenticOps practices, including evaluation, observability, guardrails, security, performance optimisation, CI/CD, versioning, rollback, drift detection • Design and implement testing strategies for non-deterministic behaviour • Stay abreast of the latest AI research trends and integrate them into our products • Maintain documentation for architectures, APIs and operational procedures • Communicate progress, blockers, technical approaches, challenges and potential solutions to technical colleagues and non-technical stakeholders.
🎯 Requirements
• Educational background in Computer Science, Data Science, or similar • Professional experience in Data Science, AI/ML Engineering, Software Engineering, or related technical roles • High proficiency in Python and working knowledge of SQL • Experience building API services using FastAPI, Flask or similar • Experience with containerisation, cloud platforms and CI/CD • Experience with AgenticOps best practices, including evaluation, observability, safety guardrails, security, performance optimisation • Experience designing and building complex multi-agent systems using at least one of the major agent frameworks (e.g. ADK, AutoGen, LangGraph) • Understanding of agent memory and state management patterns • Familiarity with MCP servers • Quality-first, test-driven mindset with focus on automated testing • Deep understanding of LLMs, foundation models and embeddings • Experience with structured and multi-modal data processing • Experience building and evaluating production-grade RAG systems • Practical experience with machine learning / deep learning models • Excellent communication skills with technical / non-technical audiences • Strong documentation and knowledge-sharing practices.
🏖️ Benefits
• Truly flexible working • Wellbeing and inclusivity prioritization • Safe environment for innovation and growth
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