Data Scientist - Agentic Systems

Requirements:

• 7+ years of experience in AI/ML engineering and Data Science, with exposure to generative AI, agents and classical ML.
• 3+ years of hands-on experience with generative large language models and agents.
• Proven experience managing the end-to-end MLOps lifecycle and deploying models at scale.
• Proven experience leveraging AI coding agents within an agentic SDLC to accelerate feature delivery.
• Hold a B. Sc., B. Eng., M. Sc., M. Eng., PhD, or equivalent in Computer Science, Physics, Statistics, Mathematics, or a related field.
• Deeply passionate about AI, staying continuously up-to-date with the latest developments in foundational models, agentic approaches, and classical ML.
• Team-oriented, proactive, and collaborative, thriving in a fast-paced environment where roles are fluid and multidisciplinary.
• A great communicator, able to present complex findings clearly to both technical and non-technical audiences.
• Able to communicate in English at the level of C1+.
• Ready to span their work times across the Central European Timezone on occasion, when necessary.

Technical Skills:

• Programming: Advanced production-grade proficiency in Python. Additional proficiency in TypeScript is considered an advantage.
• AI/GenAI Frameworks: Deep expertise with LangChain or similar LLM orchestration frameworks, agentic system design, and state machine logic.
• Cloud Platforms: Practical experience designing cloud solutions on AWS (familiarity with multicloud is a plus), utilising GenAI-specific services (e. g., Amazon Bedrock, SageMaker).
• Databases: Strong knowledge of vector databases (e. g., AWS OpenSearch), graph databases, Snowflake, SQL, NoSQL, and data lakes.
• Software Engineering Best Practices: Expertise in API design, CI/CD, Docker, Infrastructure as Code, and rigorous automated testing.
• Data Skills: Strong background in statistics, data manipulation, synthetic data generation, and feature engineering.

Capabilities:

• Problem-Solving Skills: Excellent analytical skills to tackle complex engineering and statistical challenges.
• Ownership and Leadership: Deep sense of accountability, eager to define architectural patterns, and able to step into a Tech Lead role when necessary.
• Consulting: Ability to work closely with stakeholders across the enterprise to consult on the technological approaches to their business problems.
• Ethics: Strong understanding of biases, fairness, hallucination mitigation, and responsible AI deployment.