{"id":3376925,"url":"https://alion.io/job/indosat-ooredoo-hutchison-ml-ops-engineers","title":"ML Ops Engineers","company":{"id":49933,"name":"Indosat Ooredoo Hutchison","domain":"ioh.co.id","url":"https://alion.io/company/indosat-ooredoo-hutchison","size_band":"501-1000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"SuccessFactors","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Indonesia"],"countries":["ID"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":19500,"max_usd":51000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":2365},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"CI/CD","optional":false},{"name":"DLP","optional":false},{"name":"Docker","optional":false},{"name":"GCP","optional":false},{"name":"GitLab CI","optional":false},{"name":"GitOps","optional":false},{"name":"Google BigQuery","optional":false},{"name":"Google Cloud Run","optional":false},{"name":"Google Cloud Spanner","optional":false},{"name":"Google GKE","optional":false},{"name":"IAM","optional":false},{"name":"Kubeflow","optional":false},{"name":"Linux","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Machine Learning","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Python","optional":false},{"name":"Vertex AI","optional":false},{"name":"Zero Trust","optional":false},{"name":"AutoGen","optional":true},{"name":"CrewAI","optional":true},{"name":"DeepEval","optional":true},{"name":"Google ADK","optional":true},{"name":"GraphDB","optional":true},{"name":"Kubernetes","optional":true},{"name":"LangChain","optional":true},{"name":"LangGraph","optional":true},{"name":"RAG","optional":true},{"name":"Ragas","optional":true},{"name":"Redis","optional":true}],"status":"live","first_seen_at":"2026-10-28T03:05:23Z","employer_posted_date":"2026-10-27","last_verified_at":"2026-11-03T13:58:16Z","board_verified":true,"closed_at":null,"days_open":6,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":6},"description":"Role Overview\nAs 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.\nKey Responsibilities\nArchitect 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.\nOptimize 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.\nDrive 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).\nEnforce 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.\nChampion Blast Radius Governance: Conduct comprehensive pre-deployment verification checklists for IAM policies, dependencies, and external partner integrations to guarantee zero service disruption.\nStandardize CI/CD Automation: Build and maintain reusable GitLab CI/CD runner pipelines for pipeline compilation, automated testing, container image packaging, and progressive environment promotion.\nSecure 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.\nSupport 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.\nLead 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.\nFoster 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.\nQualification\nProfessional Experience & Technical Qualifications\nEducation: Bachelor's or Master's degree in Computer Science, Information Technology, Computer Engineering, Data Science, or a related technical discipline.\nExperience: 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.\nCloud & Big Data Mastery:Deep operational expertise with Google Cloud Platform, specifically Vertex AI, BigQuery, Cloud Storage, IAM, and VPC Service Controls.\nStrong experience in BigQuery performance tuning, partitioning, clustering, and stored procedures.\n\nML Pipeline & Orchestration:Proven track record of designing, building, compiling, and monitoring production Kubeflow Pipelines (KFP) or Vertex AI Pipelines.\nDeep familiarity with model registry workflows, artifact versioning, and lifecycle management.\n\nProgramming & Automation:Expert-level proficiency in Python, including object-oriented programming, packaging, data structures, and cloud SDK integrations.\nStrong foundation in Linux environments and shell scripting.\n\nDevOps & CI/CD Tooling:Hands-on expertise building enterprise CI/CD workflows using GitLab CI/CD, Docker containerization, Helm, and GitOps methodologies.\nExperience managing private package repositories using Google Artifact Registry.\n\nSecurity & Governance Acumen:Practical experience securing day-to-day engineering AI harnesses, managing service account tokens, and implementing Data Loss Prevention (DLP) best practices.\n\nCertifications:Google Cloud Certified Professional Machine Learning Engineer (Preferred / Nice to have).\nGoogle Cloud Certified Professional Data Engineer (Preferred / Nice to have).\nGoogle Cloud Certified Professional Cloud Architect (Preferred / Nice to have).\n\nValue-Add / Bonus Qualifications:Experience with Agentic AI engineering frameworks (Google ADK, LangGraph, AutoGen, CrewAI, or MCP servers).\nFamiliarity with RAG evaluation frameworks (such as Ragas or DeepEval) and vector databases.\nExposure to Cloud Spanner, Redis/Memorystore, or GraphDB integrations.\n\nBehavioral Competencies\nProblem-Solving Agility: Exceptional analytical and diagnostic skills with the ability to resolve complex infrastructure, pipeline, and security challenges under fast-paced operational conditions.\nProactive Ownership Mindset: High sense of accountability, initiative, and drive to transform operational lessons into scalable, automated standards.\nCollaborative Communication: Strong communication and interpersonal skills to collaborate effectively across multidisciplinary squads, data science teams, and vendor partners.\nContinuous Learning & Adaptability: Eagerness to stay ahead of the curve in emerging MLOps practices, GenAI platform engineering, and modern cloud technologies.","description_format":"text","description_chars":5875,"description_truncated":false,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":["Continuous learning"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Mobile 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