CYS_Software Engineer_TP
Pubblicato il 29-10-2026 - Leonardo in Roma
Job Description
Leonardo is an international industrial group, among the world's leading companies in Aerospace, Defense and Security, which creates multi-domain technological capabilities in Helicopters, Aircraft, Aerostructures, Electronics, Cyber Security and Space. With over 60,000 employees worldwide, the company has a solid industrial presence in Italy, the United Kingdom, Poland, the United States, and operates in 150 countries also through controlled companies, joint ventures and shareholdings. A protagonist in the main strategic programs at a global level, it is a technological and industrial partner of Governments, Defense Administrations, Institutions and companies. Within the Cyber & Security Solutions Area, we are looking for a Software Engineer for data, cloud and ML services for our Genoa / Rome Laurentina office. Below is a list of the main activities foreseen for the role:
- Develop microservices for data ingestion, transformation, API exposure and processing
- Implement services for batch and streaming data processing with integration between the two paradigms
- Develop RESTful, GraphQL and gRPC APIs for data access and analytics query execution
- Implement services for cloud infrastructure management (compute, storage, networking)
- Develop services for orchestration and provisioning of cloud resources
- Implement services for security posture monitoring and compliance checking
- Develop components for cost tracking, resource optimization and billing
- Implement services for ML lifecycle management (training, evaluation, deployment, monitoring)
- Develop services for model registry, versioning and metadata tracking
- Develop APIs for model serving and inference with support for batch and real-time predictions
- Implement services for feature store management and feature engineering pipelines
- Implement services for metadata management and data catalog integration
- Develop components for data quality validation and monitoring
- Integrate with data lakehouse for unified batch-streaming storage
- Integrate with cloud providers APIs (OpenStack, AWS, Azure) for multi-cloud scenarios
- Implement services for disaster recovery automation and backup orchestration
- Develop Kubernetes operators for custom resource management
- Implement caching strategies and query optimization for performance
- Develop services for data lineage tracking and impact analysis
- Implement services for model monitoring (drift detection, performance tracking, data quality)
- Develop services for automated retraining pipelines and continuous learning
- Ensure scalability, reliability and security for data services, cloud services and ML workloads
- Implement patterns for fault tolerance, retry mechanisms and error handling
- Implement testing automation and CI/CD pipelines for cloud services, data services and ML pipelines
- Maintain high code quality standards through testing and code review
- Collaborate with data engineers, infrastructure team, data scientists and ML engineers for end-to-end implementation Education Degree in Computer Engineering, Computer Science or equivalent. Seniority Expert (2 to 5 years of experience in the role, or more than 5 years of experience in similar roles)
Technical knowledge and skills
- Backend development with enterprise languages (Java, Python, Scala, Go) for data platforms, cloud platforms and ML platforms
- Data processing with modern frameworks (Apache Spark, Apache Flink)
- Event-driven architectures for data streaming and real-time processing
- Cloud platforms APIs (OpenStack, AWS/Azure SDKs) and resource management
- Kubernetes and container orchestration with operators pattern
- Infrastructure as Code (Terraform, Pulumi) and automation
- Cloud-native microservices with service mesh integration
- MLOps practices for model lifecycle automation
- Model serving frameworks (TensorFlow Serving, TorchServe, Triton Inference Server)
- ML orchestration tools (Kubeflow, MLflow) and experiment tracking
- Feature stores (Feast, Tecton) and feature engineering pipelines
- API development (RESTful, GraphQL, gRPC) for data services, infrastructure services and ML services
- Relational and NoSQL databases optimized for analytics (columnar, document, wide-column)
- Data lakehouse integration (Delta Lake, Apache Iceberg) with ACID semantics
- Security automation (policy enforcement, compliance scanning, secrets management)
- Distributed caching (Redis, Memcached) for performance optimization
- API design for infrastructure services and ML services with versioning and backward compatibility Behavioral skills
- Autonomy in managing complex multi-component tasks
- Good communication skills and analytical problem solving
- Orientation towards code quality, data quality, automation, infrastructure as code, performance and scalability
- Security mindset for cloud environments
- Effective collaboration in cross-functional teams (backend, data engineering, analytics, infrastructure, M