Senior Technology Engineer

Job description / Role

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 and 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, or cloud engineering.
Proven experience managing enterprise-scale platforms.