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 (OpenShift + 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.
Key Responsibilities Platform Engineering & Operations 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) Automation & Orchestration 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 Container & Cloud Platform Management Manage and optimize OpenShift / Kubernetes environments Work across multi-cloud (AWS, Azure, GCP) infrastructure Ensure efficient resource utilization and cost optimisation MLOps / Data Platform Support Enable smooth ML model deployment and lifecycle management Support tools like OpenShift AI, SageMaker, or similar platforms Ensure reproducibility and reliability of data science workflows Monitoring & Reliability Implement monitoring using Prometheus, Grafana, ELK stack Define SLAs, SLOs, and ensure platform meets reliability standards Drive proactive incident prevention (not reactive firefighting) Collaboration & Governance Work closely with Data Scientists, DevOps, and Platform teams Ensure adherence to security, compliance, and governance standards Act as a technical SME for DSP operations Mandatory Skills (Non-Negotiable) Strong experience in OpenShift / Kubernetes Hands-on experience in multi-cloud environments (AWS/Azure/GCP) Expertise in automation (Terraform, Ansible, Jenkins, GitOps) Strong knowledge of CI/CD pipelines and DevOps practices Experience in Python or scripting (Bash/Shell) Experience with monitoring tools (Prometheus, Grafana, ELK) Good to Have Experience in MLOps / AI platforms (OpenShift AI, SageMaker, Bedrock) Exposure to LLM deployment / inference platforms (vLLM, Triton, etc.
) Knowledge of data pipelines and big data ecosystems Banking or financial services experience Experience Required 6–10 years of relevant experience in Platform Engineering / DevOps / Cloud Engineering Proven experience managing enterprise-scale platforms