ML Ops Engineers

As a Senior AI/MLOps Engineer specializing in ML Pipeline & Platform Engineering, you will play a pivotal role in scaling Indosat Ooredoo Hutchison's AI and data intelligence capabilities across our digital ecosystem. In this role, you will be responsible for the end-to-end design, automation, and operational management of high-throughput machine learning pipelines and cloud platform environments on Google Cloud Platform (GCP). You will empower data scientists and AI engineers by building robust CI/CD deployment automation, optimizing high-scale BigQuery workflows, enforcing zero-trust platform security guardrails, and supporting emerging Agentic AI platform integrations to accelerate Indonesia's digital transformation.

Key Responsibilities

• Architect and Operate ML Pipelines: Design, build, compile, and maintain enterprise-grade Kubeflow Pipelines (KFP) and Vertex AI Pipelines for automated training, evaluation, continuous batch inference, and automated retraining.
• Optimize High-Scale Data Workflows: Engineer and optimize large-scale BigQuery data extraction, partitioned and clustered dataset transformations, and automated data pipelines handling multi-million row operational datasets.
• Drive Platform Provisioning & Governance: Review Architectural Requirement Specifications (ARS) and execute infrastructure provisioning across GCP services (Vertex AI, BigQuery, Cloud Run, Cloud Spanner, Cloud Storage, and GKE).
• Enforce Platform Security & Guardrails: Implement least-privileged Identity & Access Management (IAM), per-pipeline service account bindings, and VPC Service Controls (VPC-SC) perimeters to prevent unauthorized data access and egress leaks.
• Champion Blast Radius Governance: Conduct comprehensive pre-deployment verification checklists for IAM policies, dependencies, and external partner integrations to guarantee zero service disruption.
• Standardize CI/CD Automation: Build and maintain reusable GitLab CI/CD runner pipelines for pipeline compilation, automated testing, container image packaging, and progressive environment promotion.
• Secure AI Tooling & Developer Harnesses: Establish security guardrails and governance policies for internal AI tooling, CLI assistants, and IDE developer harnesses, ensuring secret isolation, token sanitization, and auditability.
• Support Emerging Agentic AI Integrations: Enable operational runtime configurations and backend service connectivity for Agentic AI frameworks, Model Context Protocol (MCP) integrations, and conversational agent deployments.
• Lead Technical Problem Resolution: Troubleshoot complex container dependency builds, private package repository configurations (Google Artifact Registry), and runtime environment discrepancies across Workbench and Cloud Build runners.
• Foster Cross-Functional Collaboration: Partner closely with Data Governance, Cloud Operations, Enterprise Security, and external technology partners to ensure platform compliance, high availability, and rapid time-to-market.