Allied Command Transformation (ACT) needs to develop and implement a data science and AI capability, focusing on scalable data engineering and software systems to support AI initiatives, particularly Large Language Models (LLMs), to maintain and enhance the military relevance and effectiveness of the Alliance.
Requirements
- Minimum 4 years of proven work experience as a Data Scientist, Machine Learning Engineer, Data Engineer, or Software Engineer, with a strong emphasis on distributed systems, cloud- based architectures, developing operational AI/ML solutions, and designing API-based infrastructures, microservices architectures, and containerized applications (e.g., Docker, Kubernetes).
- Demonstrated experience working with GenAI , in particular LLMs, including preprocessing data, fine-tuning, and deployment in secure and scalable environments to include AI/ML frameworks such as TensorFlow, PyTorch , or scikit-learn.
- Proven expertise in programming languages such as Python, Java, or Scala, with demonstrated experience in software engineering practices (e.g., version control, CI/CD pipelines, containerization).
- Experience building and optimizing data pipelines, ETL processes, and real-time streaming solutions using tools like Apache Airflow, Kafka, Spark, or equivalent.
- Knowledge of applied AI principles, particularly in implementing AI systems for operational decision support and analyzing unstructured data (e.g., text, imagery).
- Ability to architect and maintain scalable data lakes, data warehouses, or distributed storage systems (e.g., Delta Lake, Snowflake, Hadoop, or NoSQL solutions).
- Demonstrated understanding of data security, privacy, and sovereignty issues, particularly in military or international environments, ensuring compliance with NATO operational and ethical standards.
Responsibilities
- Contribute to the development and implementation of an enabling data science and AI capability at HQ SACT and across the NATO Enterprise, with a specific focus on scalable data engineering and software systems to support AI initiatives.
- Design, develop, and maintain robust data pipelines and architectures to manage the ingestion, transformation, and processing of structured and unstructured data for large Language Model (LLM)-based applications and other AI systems.
- Lead efforts to optimize data delivery and automate data engineering processes, proposing enhancements to infrastructure to improve scalability, efficiency, and reliability in support of LLM deployments.
- Build API-based infrastructure and frameworks that enable seamless integration of LLMs and ML models with operational systems, ensuring performance, security, and interoperability with NATO environments.
- Support the development, testing, and validation of microservices and containerized applications to operationalize AI/ML capabilities, including deployment of LLM use cases within NATO.
- Implement distributed data storage and processing systems (e.g., cloud-based or hybrid architectures) that align with NATO standards and enable scalable use of LLMs across the enterprise.
- Develop tools and systems to improve data accessibility, enabling data scientists and analysts to efficiently interact with and query data for training, inference, and analytics.
Other
- Professional experience in NATO environments or familiarity with NATO processes, organizational culture, and decision-making structures.
- Ability to translate operational problems into practical AI/ML solutions tailored for military and civilian teams.
- Proven ability to collaborate effectively within multidisciplinary teams, including coordinating with data scientists, software engineers, and system architects on cross-functional projects.
- Strong oral and written communication skills, with the ability to brief non-technical audiences and mentor staff in AI engineering, data science, and software development concepts.
- Experience leveraging open-source frameworks and publicly available datasets to develop innovative AI and data engineering solutions for operational or analytical use cases.
