ML Ops Engineers

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.