{"id":2564918,"url":"https://alion.io/job/capgemini-vertex-ai-gcp-architect","title":"Vertex AI & GCP Architect","company":{"id":145,"name":"Capgemini","domain":"capgemini.com","url":"https://alion.io/company/capgemini","size_band":"5000+","is_staffing_agency":false,"employer_type":"services","is_intermediary":false,"listed_via":null,"ats_vendor":"Career site","truth_index":{"grade":"B","score":80,"open_postings":1756,"ghost_share":0,"stale_share":0.782,"repost_share":0.003,"time_to_fill_p50_days":28,"computed_at":"2026-10-19T05:45:15Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Pune, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":24000,"max_usd":60000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":10},"experience_years_min":12,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"BigQuery","optional":false},{"name":"Docker","optional":false},{"name":"Feature Store","optional":false},{"name":"GCP","optional":false},{"name":"Google BigQuery","optional":false},{"name":"Google GKE","optional":false},{"name":"IAM","optional":false},{"name":"Kubeflow","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Scikit-learn","optional":false},{"name":"TensorFlow","optional":false},{"name":"Terraform","optional":false},{"name":"Vertex AI","optional":false},{"name":"Kubernetes","optional":true}],"status":"live","first_seen_at":"2026-10-14T00:35:04Z","employer_posted_date":"2026-10-14","last_verified_at":"2026-10-19T14:01:17Z","board_verified":true,"closed_at":null,"days_open":5,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":5},"description":"Choosing Capgemini means choosing a company where you will be empowered to shape your career in the way you’d like, where you’ll be supported and inspired by a collaborative community of colleagues around the world, and where you’ll be able to reimagine what’s possible. Join us and help the world’s leading organizations unlock the value of technology and build a more sustainable, more inclusive world.\nYour Role\nWe are seeking an experienced Vertex AI Engineer and Cloud Delivery Lead to drive the design, deployment, and operationalisation of machine learning solutions on Google Cloud. This role bridges AI/ML engineering and cloud delivery, ensuring that models and pipelines reach production reliably, securely, and at scale.\nKey Responsibilities:\nDesign and implement end-to-end MLOps pipelines on Vertex AI, including data ingestion, model training, evaluation, and deployment\nBuild and manage Vertex AI Pipelines (Kubeflow Pipelines) for automated model training and retraining workflows\nDeploy and manage models using Vertex AI Model Registry, Endpoints, and Batch Prediction services\nImplement feature engineering workflows using Vertex AI Feature Store\nDevelop GCP-native integrations connecting Vertex AI with BigQuery, Dataflow, Cloud Storage, and Pub/Sub\nManage infrastructure for ML workloads using Terraform, ensuring reproducible and version-controlled environments\nConfigure IAM policies for Vertex AI workloads including service account governance and VPC Service Controls\nLead cloud delivery activities: sprint planning, release management, environment promotion, and stakeholder communication\nEstablish model monitoring using Vertex AI Model Monitoring for data drift and skew detection\nCollaborate with data scientists to containerise experiments and promote models through dev/staging/production\nDrive adoption of GKE for model serving workloads where custom inference infrastructure is required\nYour Profile\nTotal Years of Experinece Min.12 Years\n4+ years of experience with GCP, including 2+ years hands-on with Vertex AI\nStrong proficiency in Python and ML frameworks (TensorFlow, PyTorch, Scikit-learn)\nExperience building Vertex AI Pipelines and managing model lifecycle in Vertex AI Model Registry\nSolid Terraform skills for provisioning Vertex AI, GCS, BigQuery, and associated infrastructure\nGood understanding of GCP IAM, particularly for securing ML pipelines and data access\nExperience with GCP-native development patterns and event-driven architectures\nDemonstrated cloud delivery experience including planning, execution, and stakeholder management\nFamiliarity with containerisation (Docker) and GKE for model serving\nWhat you'll love about working here\nYou can shape your career with us. We offer a range of career paths and internal opportunities within Capgemini group. You will also get personalized career guidance from our leaders.\nYou will get comprehensive wellness benefits including health checks, telemedicine, insurance with top-ups, elder care, partner coverage or new parent support via flexible work.\nAt Capgemini, you can work on cutting-edge projects in tech and engineering with industry leaders or create solutions to overcome societal and environmental challenges.\nCapgemini is an AI-powered global business and technology transformation partner, delivering tangible business value. We imagine the future of organizations and make it real with AI, technology and people. With our strong heritage of nearly 60 years, we are a responsible and diverse group of 420,000 team members in more than 50 countries. We deliver end-to-end services and solutions with our deep industry expertise and strong partner ecosystem, leveraging our capabilities across strategy, technology, design, engineering and business operations. 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