About The Company
Leo Technologies is a pioneering technology company dedicated to advancing artificial intelligence and machine learning solutions. With a focus on innovation, Leo Technologies develops cutting-edge SaaS platforms that leverage the power of cloud-native architectures to deliver scalable, reliable, and secure AI-driven services. Committed to fostering a collaborative and dynamic work environment, Leo Technologies emphasizes continuous learning, technological excellence, and impactful solutions that transform industries and empower clients worldwide.
About The Role
We are seeking a highly skilled Senior DevOps Engineer with deep expertise in Kubernetes, complemented by significant experience in MLOps (Machine Learning Operations) and LLMOps (Large Language Model Operations). This pivotal role involves designing, building, and maintaining robust infrastructure to support machine learning and AI applications within a cloud-native environment. The ideal candidate will have a strong background in managing SaaS applications in Kubernetes, with a passion for optimizing infrastructure to facilitate model training, deployment, and scaling. You will collaborate closely with data scientists and ML engineers to streamline deployment pipelines, ensure system reliability, and enhance performance of AI services.
Qualifications
- Bachelor's degree in Computer Science, Engineering, or a related field.
- Minimum of 7 years of experience managing online production environments utilizing bare metal, cloud, and container technologies.
- At least 4 years of experience managing production Kubernetes infrastructure, with exposure to cloud solutions such as EKS, AKS, and GKE.
- Strong proficiency with Docker, including container image creation and management.
- Extensive experience in architecting and managing SaaS applications within Kubernetes, especially in MLOps and LLMOps contexts.
- Deep understanding of the machine learning lifecycle, including model training, deployment, monitoring, and scaling, particularly using AWS SageMaker.
- Experience with MLOps tools such as Kubeflow, MLflow, or similar frameworks, and their integration into Kubernetes environments.
- Familiarity with LLMOps, including deployment and management of large language models in production.
- Proficiency in scripting languages like Python.
- Experience with infrastructure automation tools such as Terraform and CloudFormation.
- Working knowledge of CI/CD pipelines, especially with GitHub and GitHub Actions.
- Experience with monitoring and logging platforms like Prometheus and Grafana.
- Solid understanding of networking, security, and Linux system administration.
- Excellent communication and collaboration skills, with experience working closely with data scientists and ML engineers.
- Familiarity with agile methodologies and environments.
- Certifications such as CKA or CKAD are considered a plus.
Responsibilities
- Architect, build, and monitor cloud-native infrastructures using Kubernetes and related technologies to support machine learning workloads.
- Design and implement scalable, reliable CI/CD pipelines for deploying ML models and AI services within Kubernetes environments.
- Assist in managing critical cloud infrastructure on AWS, focusing on model training, deployment, and scaling with AWS SageMaker.
- Collaborate with data scientists and ML engineers to automate build, testing, and deployment processes for ML models and large language models.
- Implement infrastructure solutions for continuous integration, delivery, and monitoring of AI services, ensuring seamless integration into SaaS platforms.
- Proactively monitor system performance, troubleshoot production incidents, and ensure high availability and minimal downtime.
- Optimize infrastructure for efficient training, deployment, and scaling of ML models using GPU clusters and Kubernetes.
- Develop and maintain monitoring, alerting, and logging solutions to track performance and detect anomalies in AI workloads.
- Participate in on-call rotations to support production systems and respond swiftly to incidents.
- Ensure the security, compliance, and data protection of all production environments, especially concerning sensitive AI and ML data.
- Mentor junior DevOps engineers, fostering a culture of continuous improvement and technical excellence.
Benefits
Leo Technologies offers a competitive salary package commensurate with experience, along with comprehensive health insurance plans, including medical, dental, and vision coverage. Employees enjoy flexible working hours and the possibility of remote work to promote work-life balance. The company invests in professional development through training programs, certifications, and industry conferences. Additionally, Leo Technologies provides a collaborative work environment with modern tools and resources to support innovation and growth. Employees benefit from participation in cutting-edge projects, exposure to advanced cloud-native and AI technologies, and opportunities for career advancement within a rapidly expanding organization.
Equal Opportunity
Leo Technologies is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We do not discriminate based on race, religion, gender, sexual orientation, age, disability, or any other protected status. We believe that a diverse workforce fosters innovation and drives our success, and we welcome applicants from all backgrounds to join our team and contribute to our mission of transforming AI and machine learning solutions.