Devops Engineer

Join our Data & Technology Services team as a DevOps Engineer and play a pivotal role in deploying and managing infrastructure that supports advanced AI & ML engagements for the investment banking sector. You will be instrumental in automating and optimizing our operations and development processes, ensuring seamless, scalable, and efficient delivery of services

• 2-5 years of experience in - DevOps, Azure, Kubernetes, CI/CD, Jenkins, GitHub, Docker, Terraform
• Good to have: Helm, ArgoCD GitOps
• Experience in a DevOps role, preferably within the financial services industry.
• Strong experience with cloud platforms, particularly Azure.
• Proficiency in scripting and automation using Python, Bash, or PowerShell.
• Experience with CI/CD tools such as Azure DevOps.
• Experience with Azure Kubernetes Service, Azure Storage, Azure Redis, and other Azure cloud related technologies.
• Knowledge of containerization and orchestration tools such as Docker and Kubernetes.
• Strong understanding of networking, security, and infrastructure management. E.g., Terraform.
• Implement backup and disaster recovery strategies and participate in annual DR tests and assist with executing the DR test plan. Should have a good understanding of high availability options in Azure.
• Excellent problem-solving skills and attention to detail.
• Strong communication skills, both written and oral, with a business and technical aptitude.

Additionally, desired skills:

• Familiarity with Spark technologies (e.g., PySpark).
• Data Analytical and AI/ML experience or skills.
• Knowledge of financial securities and asset classes.
• Strong experience with cloud platforms, particularly Snowflake, or Databricks.

Key Responsibilities

• Deploying and managing cloud infrastructure to enable and support data storage, processing, and analytics.
• Developing CI/CD pipelines to build and maintain continuous integration and continuous deployment pipelines to automate software delivery and infrastructure changes.
• Managing code repositories to ensure proper version control and collaboration using tools such as Git.
• Set up monitoring tools to track performance and troubleshoot issues, ensuring availability and scalability.
• Collaboration with data teams by working closely with data scientists and data engineers to automate data pipelines and integrate machine learning models into production environments.
• Implement security best practices and ensure compliance with industry standards.
• Create detailed documentation of processes and provide training to internal teams on DevOps practices.
• Managing client relationships, effectively communicating with stakeholders, and ensuring the delivery of highquality solutions.