Responsibilities

As a Senior Infrastructure Engineer, you will be responsible for deploying, scaling, and maintenance of services at the ML/AI team. You will closely work with ML Engineers and Data Scientists as a part of a small, flexible team and will have a direct impact on the process of modernization and maturation of the platform including infrastructure architecture decisions.

What does our ML/AI team do? The team supported Generative AI solution for automation and integration https://bit.ly/3VDCrzZ

Our Team International Sr-level professionals from all over the Globe are experienced in building high-performing, scalable, enterprise-grade applications. A unique team of multinational professionals with integration, cloud, and consumer experience.

Requirements

Qualifications / Experience / Technical Skills

• 7+ years professional experience in hands-on engineering roles (DevOps/SRE), BS or MS in the CS field (or equivalent experience).

• 1+ year of experience with hosting AI models (ML flow, AWS Sagemaker, Azure AI, kubernetes)

• 1+ year of experience with ML Ops (ML flow, vector databases (qdrant), dagster)

• Kubernetes Expertise: Strong experience managing Kubernetes clusters and workloads, specifically using EKS (Elastic Kubernetes Service).

• Programming Skills: Proficiency in Python language is essential. Ability to program in other languages such as Go, Ruby, or JavaScript is a plus.

• CI/CD Tools: Experience creating scalable development and integration pipelines using CI/CD tools such as GitHub Actions, Argo Workflows, GitLab CI, or similar solutions.

• Kubernetes Deployments: Expertise in deploying Kubernetes-based services using tools like Kustomize, Helm, ArgoCD, or similar, following a GitOps approach.

  Cloud Architectures: Hands-on experience with cloud-based architectures, particularly Amazon Web Services (AWS).

• Networking and Web Services: Strong understanding of networking fundamentals and web services architecture.

• Infrastructure as Code: Experience managing complex infrastructure, including Kubernetes clusters, VPC networking, and security policies, using Infrastructure as Code tools like Terraform.

• Containerization: Hands-on experience with containers, including creating and optimizing Dockerfiles, using docker-compose, and a deep understanding of related technologies (kernel features, container networking, image structure, and registry).

  Software Packaging and Testing: Familiarity with software packaging tools, functional testing, security validation tools and services, and code coverage tools.

• Compliance and Security: Operating Kubernetes clusters in compliance-regulated environments and understanding kernel-level container technologies such as seccomp, namespaces, and cgroups.

• Security Regulations and Compliance: Experience with cloud and infrastructure security regulations and compliance programs, including SOC2, ISO27001, HIPAA, GDPR, and CCPA.

Soft Skills / Personal Characteristics

• Good communication and collaboration skills in international technological companies

• Readiness to work remotely with teams distributed across the world and timezones

• Good Spoken English to participate in product-related, architectural and technical discussions