9 days ago
Bucharest, RomaniaMid Level
Responsibilities
- Collaborate with global stakeholders to identify business needs and translate them into production-ready AI solutions.
- Build data transformation and ingestion infrastructure for AI/ML processes and large volumes of real-time, unstructured data.
- Transform models into production-ready APIs, microservices, and software applications.
- Build and maintain cloud and containerization infrastructure for AI development using platforms such as AWS and GCP.
- Work with data scientists, engineers, and product managers to define AI strategies and implement features.
- Monitor, test, optimize, and update deployed models while resolving bugs and maintaining accuracy and scalability.
- Apply Generative AI, LLMs, Retrieval-Augmented Generation, external tools, and multi-agent systems where they provide business value.
- Document AI processes, standards, and controls in accordance with banking data governance policies.
- Participate in sprint planning, backlog grooming, Agile ceremonies, and delivery cycles.
- Conduct code reviews and communicate technical findings through data stories and visualizations when needed.
Requirements
- Bachelor’s or master’s degree in Artificial Intelligence, Data Science, Computer Science, Information Technology, Programming and System Analysis, Computer Studies, or a related field.
- At least 3 years of professional experience as an AI Engineer.
- Strong Python proficiency, including advanced syntax, Scikit-Learn, object-oriented programming, SOLID principles, data structures, algorithms, and complexity analysis.
- Experience applying Generative AI and tuning large language models for diverse scenarios.
- Ability to architect solutions using external logic and tools, Retrieval-Augmented Generation or MCP, and develop end-to-end multi-agent systems with ADK, DialogFlow, or equivalent cloud services.
- Knowledge of Java, SQL, Git, Jenkins, software architecture, design patterns, cloud technologies, testing, monitoring, and CI/CD concepts.
- Practical understanding of machine learning theory and application.
- Experience with the end-to-end data lifecycle, advanced data engineering, cloud and traditional databases, query optimization, data preprocessing, feature creation, and data visualization using tools such as Tableau.
- Strong analytical, problem-solving, written, and verbal communication skills, with the ability to work independently and in global cross-functional teams.
- Familiarity with Agile methodologies and user story documentation.
Benefits
- Premium medical package, lunch tickets, Pluxee Card, Bookster subscription, and a 13th salary or yearly bonuses.
- Enterprise job security with a startup mentality, international exposure, a flat hierarchy, and a supportive work-life-balance culture.
- Flexible working program with openness to remote work.
- International project opportunities and flexibility to choose projects aligned with career goals.
- Access to Pluralsight, Udemy, Microsoft, and Google Cloud learning platforms, courses, certifications, mentorship, and professional development.
- Career growth opportunities and above-market salary.
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