CYS_Software Engineer_TP

Questa posizione è in Leonardo Il processo di selezione sarà interamente gestito Leonardo. Questa opportunità è disponibile in Genoa
- Italy, Rome
- Italy. -
- #

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,