Software development Engineer 5 - Devops

Key Skills

Aartificial intelligence, Amazon Elastic Compute Cloud-EC2, Amazon Simple Storage Service (S3), Ansible, AWS, Azure, Caffe, Continuous Integration & Continuous Delivery - CI/CD, Data science techniques, Employee life cycle, Git, GitLab, Google Cloud Platform (GCP), Infrastructure-as-code-IaC, Java Programming, Jenkins, Kubernetes-K8s, Lambda, Machine learning techniques, Puppet, Python Programming, PyTorch, Shell Scripting language, Software Development, TensorFlow

Job Description

We are seeking an experienced Senior MLOps Engineer with Kubernetes background to join our dynamic team. The Engineer will be responsible for bridging the gap between development, operations, and data science teams to ensure smooth deployment and operation of machine learning models in production environments. The ideal candidate will have a strong background in managing Kubernetes clusters at scale, deploying, maintaining and optimizing infrastructure for performance and reliability. Responsibilities include designing, implementing, and supporting Kubernetes clusters, automating deployment processes, monitoring system health, troubleshooting issues, and collaborating with cross-functional teams to ensure the smooth operation of our cloud-native infrastructure. The candidate should possess excellent communication skills, a proactive attitude, and a passion for staying updated with emerging technologies in the Kubernetes ecosystem. This role offers an exciting opportunity to contribute to the growth and success of our organization in a rapidly evolving cloud computing landscape.

Responsibilities

• Architecting, deploying, and maintaining Kubernetes clusters according to best practices and organizational requirements.
• Managing containerized applications using Kubernetes, including pod scheduling, scaling, updating, and rolling deployments.
• Developing and maintaining automation scripts and tools for provisioning, configuring, and managing Kubernetes infrastructure, leveraging infrastructure-as-code principles.
• Collaborate with data scientists and software engineers to design, develop, and deploy machine learning models in production environments.
• Collaborating with cross-functional teams, including developers, DevOps engineers, and system administrators, to support application deployment and integration with Kubernetes. Documenting processes, configurations, and troubleshooting procedures.
• Build and maintain scalable, reliable, and efficient machine learning pipelines for data ingestion, model training, evaluation, and deployment.
• Implement monitoring, logging, and alerting systems to ensure the health and performance of deployed models.
• Develop and maintain infrastructure as code (IaC) using tools like Terraform or CloudFormation to automate the provisioning and configuration of cloud resources.
• Staying updated with the latest Kubernetes developments, best practices, and emerging technologies to continuously improve the organization's Kubernetes infrastructure and practices.
• Implementing disaster recovery strategies and high availability configurations to ensure business continuity and resilience of Kubernetes environments.
• Implement security best practices and compliance standards for handling sensitive data in production environments.
• Providing guidance, training, and support to junior team members and stakeholders on Kubernetes concepts, best practices, and usage.

Requirements

• Tech / M.Tech degree in Computer Science from a premiere institute.
• Should have 9 - 14 years of experience in software development and operations.
• Should have excellent computer science fundamentals and a good understanding of architecture, design and performance.
• Hands-on experience with cloud platforms such as AWS, Azure, or Google Cloud, including services like EC2, S3, Lambda, Kubernetes, and managed AI/ML services.
• Experience with containerization technologies (e.g., Docker) and container orchestration platforms (e.g., Kubernetes).
• Familiarity with version control systems (e.g., Git), CI/CD pipelines (e.g., Jenkins, GitLab CI/CD), and configuration management tools (e.g., Ansible, Puppet).
• Should have good knowledge of cloud security domain
• Proficient in Java/Python, Shell.
• Should be hands-on in writing code that is reliable, and maintainable.
• Ability to work independently with strong problem-solving skills.
• Good understanding of k8s and knowledge of product life cycles and associated issues.
• Technical depth in operating systems, computer architecture and OS internals.
• Technical depth in Cloud Computing, Cloud Platforms and Services architecture and design

Good To Have

• Foundational knowledge in, and fundamentals of Machine Learning and Artificial Intelligence
• Experience with ML Lifecycle, AI Ethics, ML Frameworks like TensorFlow, Caffe, Torch, and other similar frameworks

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