AI Engineer

Company Description

Arηs Group, Part of Accenture, specializes in the management of complex public sector IT projects, including systems integration, informatics and analytics, solution implementation and program management. Our team helps lead clients through digital and information systems design, bringing expertise in a variety of areas ranging from software development, data science and security management to machine learning, cloud, and mobile development.
Arηs Group was acquired by Accenture in July 2024. 

Job Description

• Analysis, development, testing and delivery in production of proof of concepts, pilots and projects for a variety of end-users using Artificial Intelligence (AI).
• Provide advice, support and training for AI related activities for technical and non-technical users (Modelling, Design, Architecture Review, AI projects, Standards and processes, Centre of Competence)
• Prepare analysis and architecture deliverables (eg: building blocks) concerning AI to contribute to Reference Architecture and AI Data Platform

Qualifications

More than 4 years of experience in the following areas is required:

• Prompt engineering or related fields:
  • Develop and refine prompts for AI models to achieve desired outcomes
  • Test, evaluate, and iterate on prompt strategies.
  • Analyse and report on model behaviour based on different prompt designs.
• Ability to effectively communicate with business stakeholders and explain complex technical elements in a simple way.
• Technical guidance and troubleshooting including solution finding for highly complicated issues.
• Programming languages for data analytics and artificial intelligence (eg: python)
• Industrialization and sizing of AI solutions using private cloud environments, containerisation (eg: Kubernetes, ArgoCD) and CI/CD techniques (eg: Bamboo, Git, ...).
• MLOps tools, testing and quality assurance.
• Developing with reusable open-source large language models (eg: Gemma 3) and Generative AI APIs (eg: openai)

Additionally, experience in the following areas will be an asset:

• Analysis and validation of business cases and/or maturity assessment studies.
• Analysis and validation of technological orientations and ability to cope with fast changing technologies.
• Analytical thinking, by using logic and reasoning to identify patterns and gaps while analysing and exploring data to reach data-driven decision-making.
• Data modelling for data lakes and data warehouses (eg: Medallion Architecture, Star Schema and Kimball)
• SQL (eg: Oracle, SQL Server,…)
• ETL/ELT (eg: Kestra/Airflow orchestration, DBT transformations)
• Data visualisation and dashboarding tools (eg: PowerBI, Superset)