Senior Software Engineer- DevOps for Workflow Innovation in Barco Control Rooms @Barco
BCR (Barco Control Rooms)
The Barco Control Rooms business unit is making workflow and visualization solutions for the Control Room market since 1994 to help operators collect, visualize and share critical information for optimal mission-critical decision making. Today, we are still the number one choice for control room professionals who want to stay on top of their situational awareness with 12000+ installations for critical infrastructure and critical operations.
Senior Software Engineer - DevOps for Workflow Innovation @ Barco
We are seeking a hands-on DevOps Engineer to join the Barco CTRL Workflow Innovation team and support the development of a new AI-driven product from the ground up. The ideal candidate must have strong experience in GCP, cloud-native DevOps, and AI/MLOps practices, with proven ability to build reliable platforms, automation, CI/CD workflows, and deployment infrastructure for data and AI product capabilities. You will work with a globally distributed team to enable scalable, secure, and production-ready delivery of intelligent workflow solutions for Barco Control Rooms.
About the Role
As a Senior Software Engineer - DevOps in the Workflow Innovation team, you will be responsible for building and maintaining the cloud-native engineering foundation required to develop, test, deploy, and operate AI-driven product capabilities for Barco Control Rooms. This is not a classical DevOps role focused only on build pipelines and deployments; it requires hands-on experience with AI/MLOps, cloud infrastructure, data/AI workloads, automation, observability, and secure production operations. The role will also support deployment patterns where AI capabilities may need to run closer to the operational environment, including Edge AI scenarios, hybrid cloud-edge architectures, and efficient inference workflows for mission-critical control room use cases. You will collaborate with cloud partners, product owners, architects, Data & AI engineers, developers, and validation teams across locations to enable fast, reliable, and secure delivery of workflow innovation capabilities.
- Build and maintain cloud-native DevOps and AI/MLOps infrastructure for the Workflow Innovation product on GCP.
- Design and implement CI/CD workflows for backend services, data pipelines, AI/ML components, model integration, and GenAI-enabled capabilities.
- Automate infrastructure provisioning, environment management, configuration, and deployment workflows using infrastructure-as-code practices.
- Enable deployment and operation of AI/ML workloads, including model serving, inference workflows, RAG-based components, vector search services, and related data/AI services.
- Support cloud-to-edge deployment and operational patterns for AI capabilities, including lightweight model packaging, inference deployment, monitoring, and lifecycle management for Edge AI scenarios.
- Establish observability, monitoring, logging, alerting, reliability, and cost-awareness practices for cloud-native Data & AI product capabilities.
- Support secure handling of product data, secrets, access control, compliance needs, and cloud security best practices.
- Work closely with Data & AI engineers and product teams to improve developer experience, release readiness, test automation, and operational reliability.
- Guide and mentor fellow colleagues in DevOps, cloud, and AI/MLOps practices while contributing to technical discussions and engineering excellence.
Qualifications and Experience
We are seeking experience with the following technologies/domains:
Education:
B. Tech./B. E./M. E./M. Tech. in Computer Science/AI Engineering
Experience:
- 6-9 years of hands-on experience in DevOps, cloud platform engineering, SRE, AI/MLOps, or related product engineering roles.
- Strong hands-on experience with GCP is required, including relevant cloud-native compute, storage, networking, IAM, observability, and deployment services.
- Experience designing and implementing CI/CD pipelines for cloud-native applications, backend services, data pipelines, and AI/ML workloads.
- Hands-on experience with AI/MLOps practices, including model deployment, model serving, inference workflows, experiment tracking, model versioning, and release automation for AI-enabled product features.
- Experience with containerization and orchestration technologies such as Docker and Kubernetes.
- Experience with infrastructure-as-code and environment automation using tools such as Terraform, Helm, or equivalent technologies.
- Good understanding of cloud security practices, including IAM, secrets management, network security, secure deployment patterns, and least-privilege access.
- Experience setting up monitoring, logging, alerting, dashboards, SLO/SLA-oriented reliability practices, and incident response workflows.
- Exposure to data and AI platforms or services such as BigQuery, Vertex AI Vector Search, Dataflow, Dataproc, Cloud Run, GKE, Pub/Sub, vector databases, or equivalent technologies.
- Experience supporting RAG-based applications, GenAI-enabled services, model APIs, or AI inference platforms is highly desirable.
- Exposure to Edge AI concepts, hybrid cloud-edge deployments, on-device or near-device inference, model optimization, and operational constraints such as latency, reliability, connectivity, and resource usage is desirable.
- Good programming and scripting experience using Python, Bash, or similar languages, with ability to build automation and internal tooling.
- Understanding of DevSecOps practices, automated testing, quality gates, vulnerability scanning, dependency management, and secure software supply chain practices.
- Ability to collaborate with cloud partners, architects, Data & AI engineers, developers, product owners, and validation teams across geographies.
- Strong problem-solving mindset, ownership, operational discipline, and willingness to explore and adopt new technologies in AI, DevOps, and cloud engineering.