{"id":2433995,"url":"https://alion.io/job/matchatalent-mlops-engineer-machine-learning-platform-cloud-infrastructure-4","title":"MLOps Engineer (Machine Learning Platform & Cloud Infrastructure)","company":{"id":4428485,"name":"Matcha Talent","domain":"matchatalent.com","url":"https://alion.io/company/matchatalent","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Manatal","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Osaka, Japan"],"countries":["JP"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":3,"visa_sponsorship":true,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon EKS","optional":false},{"name":"Amazon SageMaker","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Azure AKS","optional":false},{"name":"CI/CD","optional":false},{"name":"CloudFormation","optional":false},{"name":"Datadog","optional":false},{"name":"Docker","optional":false},{"name":"GCP","optional":false},{"name":"GitHub Actions","optional":false},{"name":"Google GKE","optional":false},{"name":"Grafana","optional":false},{"name":"KServe","optional":false},{"name":"Kubeflow","optional":false},{"name":"Kubernetes","optional":false},{"name":"Linux","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"Prometheus","optional":false},{"name":"Python","optional":false},{"name":"Terraform","optional":false},{"name":"Vertex AI","optional":false}],"status":"live","first_seen_at":"2026-10-13T10:07:48Z","employer_posted_date":"2026-10-13","last_verified_at":"2026-10-13T18:03:34Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"This role requires the candidate to work on-site in Tokyo, Japan.\n\nClient Overview\nOur client is a global technology solutions provider specializing in Artificial Intelligence (AI), Cloud Computing, Data Engineering, and Digital Transformation (DX). The organization partners with enterprise clients across multiple industries to design, deploy, and operate scalable AI platforms that support machine learning, advanced analytics, and business automation.\nAs enterprise adoption of AI continues to accelerate, the company is expanding its MLOps Engineering team to build production-grade machine learning infrastructure, automate AI deployment pipelines, and optimize cloud-native AI platforms using modern DevOps and MLOps technologies.\n\nJob Role\nThe MLOps Engineer will design, implement, and manage enterprise machine learning infrastructure that enables efficient model development, deployment, monitoring, and lifecycle management. Working closely with Data Scientists, AI Engineers, and Cloud Engineers, this role will build scalable MLOps pipelines, automate machine learning workflows, and ensure AI models are securely deployed and maintained in production environments.\nThis position is ideal for engineers who are passionate about cloud infrastructure, DevOps, Kubernetes, and production-scale AI systems.\n\nKey Responsibilities\nDesign, build, and maintain cloud-based machine learning infrastructure using AWS, Microsoft Azure, or Google Cloud Platform (GCP).\n\nDevelop and optimize containerized AI applications using Docker and Kubernetes (EKS, AKS, or GKE).\n\nBuild and manage Infrastructure as Code (IaC) using Terraform or CloudFormation.\n\nDesign and maintain CI/CD pipelines for machine learning applications using GitHub Actions or similar tools.\n\nDevelop and automate machine learning workflows using Airflow, Kubeflow, MLflow, Vertex AI Pipelines, SageMaker Pipelines, or equivalent platforms.\n\nDeploy machine learning models into production using scalable serving frameworks such as KServe or SageMaker Endpoints.\n\nMonitor infrastructure and model performance using Prometheus, Grafana, Datadog, and other observability tools.\n\nOptimize cloud infrastructure performance, resource utilization, operational costs, and automate model retraining processes.\n\nCandidate Requirements\nBachelor's degree in Computer Science, Software Engineering, Information Technology, Artificial Intelligence, Data Engineering, or a related discipline.\n\nMinimum 3 years of experience in Software Engineering, DevOps, Cloud Engineering, MLOps, or related technical roles.\n\nStrong programming experience using Python.\n\nHands-on experience with at least one major cloud platform: AWS, Microsoft Azure, or Google Cloud Platform (GCP).\n\nPractical experience with Docker, Kubernetes, and container orchestration in production environments.\n\nExperience implementing Infrastructure as Code (IaC) using Terraform or CloudFormation.\n\nExperience designing and maintaining CI/CD pipelines using GitHub Actions or similar DevOps tools.\n\nWorking knowledge of machine learning concepts, model deployment, and AI production environments.\n\nFamiliarity with MLflow, Kubeflow, Airflow, Vertex AI, SageMaker, Prometheus, Grafana, or Datadog is highly preferred.\n\nExperience with Linux system administration and cloud cost optimization is an advantage.\n\nStrong analytical thinking, automation mindset, and problem-solving skills.\n\nBusiness-level Japanese proficiency (JLPT N2 or above) is mandatory.\n\nProfessional English communication skills are preferred.\n\nCandidates eligible for relocation and visa sponsorship are encouraged to 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