Cloud Platform DevOps Engineer - Assistant Vice President

Design & Implementation: Lead the design, implementation, and ongoing management of secure, scalable, and resilient infrastructure components.

Secret & Certificate Management: Administer and maintain secret and certificate management solutions using HashiCorp Vault, including policy definition and integration.

Database Management: Perform hands-on administration and optimization of database systems (PostgreSQL, Oracle, MongoDB), including performance tuning, backup, and recovery strategies.

Workflow Orchestration: Deploy, monitor, and troubleshoot data orchestration workflows using Apache Airflow, and develop/optimize DAGs.

Messaging Systems: Implement and manage messaging queues such as Kafka and IBM MQ, including cluster setup and configuration.

API Integrations: Develop, maintain, and troubleshoot RESTful API and SOAP integrations critical for system connectivity.

Build Automation: Implement and optimize build and deployment processes using Gradle.

Container Orchestration: Design, implement, and manage container orchestration platforms with Kubernetes and Helm, including integration with CyberArk and HashiCorp for secrets management. Create, debug, and troubleshoot Kubernetes PODs, Jobs, and Deployments using YAML.

Storage Management: Configure and manage persistent storage solutions including PVC, SONiC NAS, and S3, with an awareness of storage requirements for AI/ML workloads.

Networking & Load Balancing: Set up and maintain load balancing solutions (e.g., Nginx, HAProxy, AWS ELB/ALB, Kubernetes Ingress controllers) for high availability and performance.

Monitoring & Logging: Implement, configure, and utilize comprehensive monitoring and logging solutions (Prometheus, Grafana, ELK Stack) to ensure system health and proactively identify issues, including those relevant to AI/ML applications.

Automation & Scripting: Develop robust automation scripts and tools using Python, Bash, Go, or similar languages to streamline operations and enhance efficiency.

Incident Response: Participate actively in on-call rotations, responding to and resolving critical incidents with hands-on troubleshooting.

Documentation: Create and maintain technical documentation, architecture diagrams, and runbooks for infrastructure components and processes.

Impediment Resolution: Proactively identify and resolve technical impediments and process bottlenecks within the team and across organizational boundaries, paying special attention to unique challenges posed by AI/ML infrastructure.

Backlog Refinement: Collaborate closely with stakeholders (e.g., product owners, technical leads) to ensure a well-defined and prioritized backlog for infrastructure work, technical debt, operational improvements, and AI/ML platform needs.

Process Improvement: Drive continuous improvement in the team's agile and DevOps practices, helping them adapt and optimize their workflow for maximum efficiency and quality.

Required Qualifications:

Secret & Certificate Management: Proven hands-on experience with HashiCorp Vault (installation, configuration, policy management, integrations).

Database Administration: Strong hands-on experience with at least two of PostgreSQL, Oracle, or MongoDB (installation, tuning, replication, backup/restore).

Workflow Orchestration: Hands-on experience deploying, managing, and developing DAGs for Apache Airflow.

Messaging Systems: Solid hands-on experience with Kafka and/or IBM MQ (cluster setup, topic management, producer/consumer configuration).

Container Orchestration: In-depth hands-on experience with Kubernetes and Helm, including YAML configuration, troubleshooting PODs/Jobs/Deployments, and integrations with secrets management (CyberArk, HashiCorp).

Storage Management: Practical experience with Kubernetes PVCs, Persistent Volumes, S3, and/or enterprise NAS solutions (e.g., SONiC NAS).

Monitoring & Logging: Strong hands-on experience with Prometheus, Grafana, and the ELK Stack (setup, dashboard creation, query optimization, alert configuration).

Scripting & Automation: High proficiency in Python, Bash, or Go for automation, tooling development, and system administration.

Cloud Platforms: Extensive hands-on experience with at least one major cloud provider (AWS, Azure, GCP).

Infrastructure as Code (IaC): Proficiency with IaC tools such as Terraform or Ansible.

CI/CD: Experience designing, implementing, and maintaining CI/CD pipelines (e.g., Jenkins, GitLab CI, GitHub Actions).

API Integration: Experience with RESTful API and SOAP web services.

Build Tools: Proficiency with Gradle for build automation.

AI/ML Infrastructure Concepts: Understanding of the specific infrastructure requirements for deploying, managing, and scaling Artificial Intelligence and Machine Learning workloads (e.g., GPU resources, specialized storage, MLOps pipelines).

Data for AI/ML: Awareness of data management strategies and data governance principles relevant to AI/ML models and training datasets.

Monitoring AI/ML Systems: Familiarity with metrics and monitoring approaches for the performance and health of AI/ML applications and their underlying infrastructure.

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