Senior Software Engineer- Data & AI 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 12,000+ installations for critical infrastructure and critical operations.
Senior Software Engineer - Data & AI for Workflow Innovation @ Barco
We are seeking a hands-on Data & AI Engineer to join the Barco CTRL Workflow Innovation team and help build a new AI-driven product from the ground up. The ideal candidate must have strong experience in GCP and cloud-native services, with proven experience in data engineering, data pipelines, and applying AI/ML engineering to production product features. You will work with a globally distributed team to design and develop scalable, secure, and reliable production-ready data and AI capabilities that enable automation and intelligent workflows for Barco Control Rooms.
About the Role
As a Senior Software Engineer - Data & AI in the Workflow Innovation team, you will contribute to the design and development of AI-driven product capabilities for Barco Control Rooms. This role requires the ability to understand product data, extract meaningful insights, and apply AI/ML engineering to create intelligent, workflow-focused features. You will collaborate with cloud partners, product owners, architects, developers, and validation teams across locations to build scalable, reliable, and secure capabilities that transform control room workflows and deliver meaningful customer outcomes.
- Collaborate with cloud partners, product owners, architects, developers, and validation teams across locations to define and build data and AI product capabilities.
- Design and develop cloud-native data and AI capabilities on GCP, with focus on scalability, reliability, security, and maintainability.
- Design and implement AI capabilities spanning cloud and Edge AI deployments, including model optimization, inference workflows, and hybrid cloud-edge architectures where operational requirements demand local intelligence.
- Work with product data to identify patterns, opportunities, and use cases where AI/ML can improve workflow efficiency, automation, and decision support.
- Apply AI/ML engineering practices to build and integrate intelligent features into production product capabilities.
- Contribute to data modelling, data quality, governance, and secure handling of data across product features.
- Apply DevOps practices, including Git workflows, CI/CD, test automation, and release readiness.
- Guide and mentor fellow colleagues in AI and data skills, while actively 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 Data & AI engineering, cloud-native product development, or related product engineering roles.
- Strong hands-on experience with GCP is required, including relevant cloud-native data, AI/ML, storage, compute, and integration services.
- Strong programming experience in Python, with good exposure to Databases, REST APIs, and modern backend service development.
- Experience designing and building scalable data pipelines, data workflows, and data models for product use cases, including batch, near real-time, or streaming data patterns.
- Experience applying AI/ML engineering to real product features, including model integration, inference workflows, prompt/RAG-based workflows, vector search, or GenAI-enabled capabilities.
- Experience designing AI solutions that operate across cloud and edge environments, balancing latency, reliability, data locality, and operational resilience requirements.
- Good understanding of data platforms and databases/data warehouses, with a preference for Google BigQuery; experience with Databricks, Snowflake, SQL Server, or equivalent technologies is also valued.
- Exposure to modern data engineering and orchestration tools.
- Experience with DevOps and MLOps practices, including Git workflows, CI/CD pipelines, automated testing, Docker, Kubernetes, and infrastructure-as-code concepts.
- Understanding of data quality, data governance, data security, metadata management, and secure handling of product data.
- Ability to translate ambiguous products and business needs into scalable data and AI architectures.
- Strong problem-solving mindset, ownership, and willingness to explore and adopt new technologies in AI, data, and cloud engineering.
- Good communication and mentoring skills, with the ability to guide fellow engineers in Data & AI practices.
