AI Engineer for the Analytics Plateau

• Design and implement LLM tool interfaces, such as function-calling schemas, tool adapters, or emerging protocols like MCP (Model-View-Controller).
• Collaborate with cross-functional teams to ensure seamless integration of AI components into existing systems.
• Develop and maintain APIs (REST/GraphQL) for AI model interaction and integration.

AI Development:

• Implement and improve AI workflows, including model prompting, tool usage, evaluation, and error-handling loops.
• Utilize classic NLP techniques (e.g., tokenization, tagging, parsing, entity extraction) when deterministic or hybrid approaches are required.
• Build and maintain lightweight data pipelines or preprocessing scripts for structured and unstructured data used in LLM workflows.

Software Engineering Best Practices:

• Apply software engineering best practices, including testing, documentation, versioning, and observability.
• Contribute to internal knowledge sharing on LLM and NLP capabilities and best practices.

Data Management:

• Utilize graph databases, such as Neo4j, for knowledge structures or relationship modeling.
• Work with data using basic data engineering concepts (ETL/ELT, preprocessing, handling structured/unstructured text).

• Strong Python software development skills, including code quality, testing, debugging, and modular design.
• Deep understanding of LLMs, their limitations, constraints, and the ability to design agentic workflows.
• Proficiency in LLM tool interfaces and understanding of how models interact with external systems (e.g., function calling, tool schemas, adapters, protocols such as MCP or equivalents).
• Experience with AI model integration, deployment, and monitoring in production environments.
• Familiarity with classic NLP techniques and tools, such as spaCy, for hybrid or deterministic components.
• Sound understanding of REST/GraphQL APIs, microservice interaction, and integrating AI components into existing systems.
• Knowledge of containerization and orchestration tools, such as Docker, Jenkins, and Kubernetes.

Nice-to-Have Skills:

• Experience with graph databases (e.g., Neo4j) for knowledge modeling.
• Exposure to vector stores, embeddings, or retrieval pipelines.
• Understanding of evaluation strategies for LLM-based systems.
• Familiarity with cloud platforms (e.g., AWS, Azure, GCP) and their AI/ML services.
• Knowledge of AI ethics, fairness, and interpretability principles and practices., * B.Sc. or M.Sc. in Computer Science, Artificial Intelligence, Data Science, or a related field.
• Proven experience in AI engineering, with a focus on integrating and developing LLMs and NLP solutions.
• Strong programming skills in Python and familiarity with other relevant languages.
• Excellent problem-solving skills and the ability to work in a team environment.
• Proficiency in English; knowledge of other languages is a plus.

In this role, you will have the opportunity to work on challenging and rewarding projects, driving innovation and digital transformation in our organization. If you are passionate about AI and eager to make a significant impact, we encourage you to apply., This job requires an awareness of any potential compliance risks and a commitment to act with integrity, as the foundation for the Company's success, reputation and sustainable growth.