Senior DevOps Engineer at Ericsson

Bengaluru, karnataka, India -

Full Time

Start Date

Immediate

Expiry Date

25 Mar, 26

Salary

0.0

Posted On

25 Dec, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Kubernetes, Cloud Services, AI/ML, CI/CD Pipelines, Data Pipeline, Hadoop, Spark, Kafka, Python, Bash, Ansible, Terraform, Security Standards, Infrastructure, Monitoring Systems, Logging Systems, MLOps

Industry

Telecommunications

Description

About this opportunity With the introduction of 5G and cloud, the role of IT Managed Services has evolved to become an enabler of new revenue opportunities, in addition to delivering efficient cloud and IT operations for service providers on their 5G journey. Join us to understand how different technologies come together to build a best-in-class solution which has made Ericsson lead the 5G evolution. We will also explain how you can be part of this outstanding culture and advance your career while creating a global impact. We believe in trust - we trust each other to do the right things! Therefore, we believe in taking decisions as close to the product and technical expertise as possible. We believe in creativity - trying new things and learning from our mistakes. We believe in sharing our insights and helping one another to build an even better user plane. We truly believe in happiness, we enjoy and feel passionate about what we do and value each other's technical competence deeply. What you will do Deploy, manage, and optimize Kubernetes clusters specifically tailored for AI/ML workloads, ensuring optimal resource allocation and scalability across different network configurations. Develop and maintain CI/CD pipelines tailored for continuous training and deployment of machine learning models, integrating tools like Kubeflow, MLflow, ArgoFlow or TensorFlow Extended (TFX) etc. Collaborate with data scientists to oversee the deployment of machine learning models and set up monitoring systems to track their performance and health in production. Design and implement data pipelines for large-scale data ingestion, processing, and analytics essential for machine learning models, utilizing distributed storage and processing technologies such as Hadoop, Spark, and Kafka. Monitor, analyse, and optimize the performance of AI/ML applications and infrastructure, focusing on reducing latency and increasing throughput for model training and inference. Ensure compliance with data privacy regulations pertinent to AI/ML projects and implement security best practices for data protection, model privacy, and secure access controls. Set up monitoring systems to track the health and performance of AI/ML models in production and implement logging systems to capture model behaviour and data quality issues. Work closely with AI/ML researchers, data scientists, and software developers to understand requirements and deliver scalable infrastructure solutions, providing DevOps expertise in system design and operational challenges. Lead infrastructure projects and mentor junior engineers and team members on best practices in DevOps, Kubernetes, cloud infrastructure, and AI/ML operations. Utilize Apache Spark for big data processing tasks to prepare and transform datasets required for machine learning model training. Implement and maintain the ELK stack (Elasticsearch, Logstash, Kibana) for logging and visualizing infrastructure and application metrics. Develop dashboards in Grafana and Kibana for real-time monitoring of data pipelines and AI/ML workflows. Design and implement robust AI/ML infrastructure using cloud services and Kubernetes to support machine learning operations (MLOps) and data processing workflows. You will bring Extensive experience with Kubernetes and cloud services (AWS, Azure, GCP, private cloud) with a focus on deploying and managing AI/ML environments. Strong proficiency in scripting and automation using languages like Python, Bash, Ansible and HasiCorp Terraform In-depth knowledge of data pipeline and workflow management tools, distributed data processing (Hadoop, Spark), and messaging systems (Kafka, RabbitMQ). Expertise in implementing CI/CD pipelines, infrastructure as code (IaC), and configuration management tools. Familiarity with security standards and data protection regulations relevant to AI/ML projects. Proven ability to design and maintain reliable and scalable infrastructure tailored for AI/ML workloads. Excellent analytical, problem-solving, and communication skills. Experience 5-8 Years Location - Bangalore

Responsibilities

The Senior DevOps Engineer will deploy, manage, and optimize Kubernetes clusters for AI/ML workloads and develop CI/CD pipelines for machine learning models. They will also collaborate with data scientists and oversee the deployment and monitoring of machine learning models in production.