Role Overview 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. Qualification Professional Experience & Technical Qualifications • Education: Bachelor's or Master's degree in Computer Science, Information Technology, Computer Engineering, Data Science, or a related technical discipline. • Experience: Minimum 5+ years of total experience in software, data, or ML engineering, with at least 3+ years of dedicated hands-on experience in MLOps, DataOps, or DevOps within enterprise production environments. • Cloud & Big Data Mastery: • Deep operational expertise with Google Cloud Platform, specifically Vertex AI, BigQuery, Cloud Storage, IAM, and VPC Service Controls. • Strong experience in BigQuery performance tuning, partitioning, clustering, and stored procedures. • ML Pipeline & Orchestration: • Proven track record of designing, building, compiling, and monitoring production Kubeflow Pipelines (KFP) or Vertex AI Pipelines. • Deep familiarity with model registry workflows, artifact versioning, and lifecycle management. • Programming & Automation: • Expert-level proficiency in Python, including object-oriented programming, packaging, data structures, and cloud SDK integrations. • Strong foundation in Linux environments and shell scripting. • DevOps & CI/CD Tooling: • Hands-on expertise building enterprise CI/CD workflows using GitLab CI/CD, Docker containerization, Helm, and GitOps methodologies. • Experience managing private package repositories using Google Artifact Registry. • Security & Governance Acumen: • Practical experience securing day-to-day engineering AI harnesses, managing service account tokens, and implementing Data Loss Prevention (DLP) best practices. • Certifications: • Google Cloud Certified Professional Machine Learning Engineer (Preferred / Nice to have). • Google Cloud Certified Professional Data Engineer (Preferred / Nice to have). • Google Cloud Certified Professional Cloud Architect (Preferred / Nice to have). • Value-Add / Bonus Qualifications: • Experience with Agentic AI engineering frameworks (Google ADK, LangGraph, AutoGen, CrewAI, or MCP servers). • Familiarity with RAG evaluation frameworks (such as Ragas or DeepEval) and vector databases. • Exposure to Cloud Spanner, Redis/Memorystore, or GraphDB integrations. Behavioral Competencies • Problem-Solving Agility: Exceptional analytical and diagnostic skills with the ability to resolve complex infrastructure, pipeline, and security challenges under fast-paced operational conditions. • Proactive Ownership Mindset: High sense of accountability, initiative, and drive to transform operational lessons into scalable, automated standards. • Collaborative Communication: Strong communication and interpersonal skills to collaborate effectively across multidisciplinary squads, data science teams, and vendor partners. • Continuous Learning & Adaptability: Eagerness to stay ahead of the curve in emerging MLOps practices, GenAI platform engineering, and modern cloud technologies.