As a Senior Software Engineer, you will architect, implement, test and deploy AI/ML Services and Systems, enabling AI Applications Development and Production deployment with focus on scaling, monitoring and performance.

The MLOps Platform unit is designing, engineering, operating, and maintaining a core system that delivers AI/ML Services and Systems, e.g. CI/CD, data exploration notebooks (Jupyter), ML model development and deployment, workflow engines, and ML frameworks (i.e. TensorFlow) for easy consumption by Ericsson products and services.

The AI/ML Platform covers infrastructure capacity and tools for all AI/ML project and system needs for Ericsson products to realize telecom AI/ML Use cases. The main approach is to integrate/extend existing private and public cloud infrastructures, and to base the toolbox components on open source software. The deployed environments are heterogeneous, so multi-cloud, hybrid-cloud, and WAN networking are also key technology areas. 

In this role, you are expected to be a very hands-on leader, functioning as an individual contributor, as well as leading others.

Key Responsibilities:

• As Architect perform detailed studies providing technical anatomy with estimates, implementation architecture/solution proposal,
• As a Cloud Native – Micro Service Architecture based developer be proficient in languages like Python, Go, Java with applications deployed in containers on Kubernetes.
• Develop/integrate/automate a core AI/ML software environment, in close collaboration with data scientist and product developers
• Operationalize and extend open source software components, covering the entire ML model life-cycle, including e.g. data transformation, model development, deployment, monitoring, re-training, security.
• Collaborate with product development teams and partners in Ericsson Businesses to industrialize a platform for machine learning models and solutions as part of Ericsson offerings including providing code, workflows and documents
• Evaluate and plan capacity of CPU, GPU, memory, storage, and networking resources to balance cost versus desired productivity and performance                          
• Develop essential Pipelines for CI/CD, Test automation scripts and tooling to help short release cycles, quality assurance, maintenance, migration, and cost-control of infrastructure deployments.
• Manage communication, planning, collaboration and feedback loops with business stakeholders.
• Model the business problem statement into AI/ML problem.
• Contribute to IPR creation for Ericsson in AI/ML
• Lead functional and technical analysis within Ericsson businesses and for strategic customers to understand ML-driven business needs and opportunities
• Lead studies and creative usage of new and/or existing data sources. Work with Data Architects to leverage existing data models and build new ones as needed.
• Provide ML Competence build-up in Ericsson Businesses and Customer Serving Units
• Develop new and apply/extend existing, concepts, methodologies, techniques for cross functional initiatives

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
• 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 sys Official notification