Senior Software Engineer

Exciting Opportunity:
It will be practically impossible for human brains to understand how to run and optimize next generation of wireless networks, i.e., /6G networks with distributed edge compute, that will drive economic and social transformation for all aspects of society. Machine Learning (ML) and other Artificial Intelligence (AI) technologies will be vital for us to handle this opportunity. We develop Ericsson MLOps framework.
Ericsson MLOps is a common and customizable MLOps framework for scaling AI adoption across Ericsson. Ericsson MLOps is intended to be embedded into different Ericsson products and services by internal stakeholders with AI use cases to facilitate an efficient end to end Life Cycle Management of ML models, including experimentation, development, deployment, monitoring and re-training. By introducing a common MLOps framework we get a company wide common environment for development and execution of ML which will increase R&D efficiency by unifying technology choices and enabling re-use and collaboration. This allows stakeholders to focus more on their use cases. Furthermore, Ericsson MLOps will enable ML deployments at scale and quality, managing large amounts of models with efficient tracking, monitoring and re-training.

Key Qualifications:

• Bachelors/Masters in Computer Science, Electrical Engineering or related disciplines from any of the reputed institutes. First Class, preferably with Distinction.
• Applied experience: 5+ years of experience with infrastructure, platforms, networking, and software systems; 3+ years of experience with leading teams of engineers and system administrators; and an overall industry experience of about 10+ years.
• Strong software engineering experience with one or more of Go, Python, Java using container-based Cloud Native - Micro Service development practices.
• Certified Kubernetes Application Developer (CKAD) is desirable
• Experience with data analytics and AI/ML systems, for example, Spark, Jupyter, Tensorflow
• Elaborate and expand AI/ML and Data science use cases from product/solution teams to fine-grained engineering requirements and implementation details for the development teams.
• Ensure the development team has sufficient details and clarity on AI/ML and Data science use cases from stake-holders.
• Experience with large scale systems, for example reliability/HA, deployment, operations, testing, and trouble-shooting.
• Experience with delivering software products, for example release management, documentation
• Experience with usage/integration of public cloud services, for example, identity and access management, key management, storage systems, CPU/GPU, private/virtual networking, and Kubernetes services.
• Experience with modern distributed systems and tooling, for example, Prometheus, Terraform, Kubernetes, Helm, Vault, CI/CD systems.
• Experience with WAN networking solutions, redundancy/fail-over, QoS, and VPN technologies.
• Strong system administration skills, Linux and Windows
• Awareness of ITIL/ITSM methodologies for operations and service delivery