Senior Technology Engineer - Data Science Platform; DSP

Role Overview

We are looking for a Senior Technology Engineer to drive platform stability, automation, and operational excellence within the Data Science Platform (DSP).

This is not a support role — this is a hands-on engineering role where you own automation, orchestration, and reliability across a hybrid cloud ecosystem (Open Shift + AWS/Azure/GCP).

You will be the backbone of DSP operations — if things break, scale poorly, or require manual intervention, that’s your problem to eliminate permanently.

• Own end-to-end technical operations of DSP infrastructure
• Ensure high availability, performance, and scalability of platform services
• Monitor system health, troubleshoot issues, and implement permanent fixes (not patchwork)

• Design and implement automation frameworks to eliminate manual processes
• Build CI/CD pipelines and automate deployment workflows
• Drive infrastructure-as-code (IaC) adoption using tools like Terraform/Ansible

• Manage and optimize Open Shift / Kubernetes environments
• Work across multi-cloud (AWS, Azure, GCP) infrastructure
• Ensure efficient resource utilization and cost optimisation

• Enable smooth ML model deployment and lifecycle management
• Support tools like Open Shift AI, Sage Maker, or similar platforms
• Ensure reproducibility and reliability of data science workflows

• Implement monitoring using Prometheus, Grafana, ELK stack
• Define SLAs, SLOs, and ensure platform meets reliability standards
• Drive proactive incident prevention (not reactive firefighting)

• Work closely with Data Scientists, Dev Ops, and Platform teams
• Ensure adherence to security, compliance, and governance standards
• Act as a technical SME for DSP operations

• Strong experience in Open Shift / Kubernetes
• Hands-on experience in multi-cloud environments (AWS/Azure/GCP)
• Expertise in automation (Terraform, Ansible, Jenkins, Git Ops)
• Strong knowledge of CI/CD pipelines and Dev Ops practices
• Experience in Python or scripting (Bash/Shell)
• Experience with monitoring tools (Prometheus, Grafana, ELK)

• Experience in MLOps / AI platforms (Open Shift AI, Sage Maker, Bedrock)
• Exposure to LLM deployment / inference platforms (vLLM, Triton, etc.)
• Knowledge of data pipelines and big data ecosystems
• Banking or financial services experience

• 6–10 years of relevant experience in Platform Engineering / Dev Ops / Cloud Engineering
• Proven experience managing enterprise-scale platforms