Azure Cloud Architect designing and implementing multi - cloud solutions for Kyndryl customers. Leading client workshops and co - creation sessions to optimize technology strategies.
About the role
- GCP Engineer specializing in Agentic AI technologies to design and maintain scalable AI platforms on Google Cloud. Working across the AI lifecycle from pipeline to infrastructure management.
Responsibilities
- Design and manage end-to-end AI agent build pipelines using Vertex AI Pipelines
- Automate data ingestion, feature engineering, training, evaluation, and deployment workflows
- Implement reproducible experiments and pipeline versioning
- Work with BigQuery for large-scale analytics and training datasets
- Manage datasets and artifacts in Google Cloud Storage
- Implement data lineage tracking for traceability and governance
- Implement Infrastructure as Code (IaC) using Cloud Build, Terraform, or similar tools for provisioning and managing GCP resources
- Build CI/CD pipelines for AI agent training and deployment
- Manage source control and collaboration using Git
- Containerize AI workloads using Docker
- Deploy batch and real-time Agent builds using Cloud Run Services, Cloud Run Jobs, or Openshift cluster/GKE (if applicable)
- Implement deployment strategies such as blue-green, canary, and rollback
- Build and deploy LLM-powered applications
- Implement Retrieval-Augmented Generation (RAG) pipelines
- Integrate and manage Vector Databases for embeddings and semantic search
- Implement AI agent/model lineage tracking and versioning
- Monitor AI agent/model performance, drift, and data quality in production
- Set up alerting and dashboards for AI agent/model health
- Design and manage secure, scalable, and cost-effective GCP infrastructure components
- Implement and enforce security best practices and IAM policies
- Proactively monitor and optimize GCP resource utilization and costs
- Troubleshoot and resolve infrastructure-related issues impacting AI workloads
- Develop production-grade AI agent services using Python or other relevant languages
- Package reusable AI components and libraries using PyPI or other appropriate methods
- Follow best practices for code quality, testing, and documentation
Requirements
- Bachelor's Degree or equivalent
- Strong experience with GCP services (Vertex AI, BigQuery, GCS, Cloud Run, GKE, Compute Engine, Cloud Networking)
- Solid understanding of AI algorithms and training workflows
- Hands-on experience with LLMs, RAG, and vector search systems
- Proficiency in Docker, Git, and CI/CD pipelines
- Strong Python programming skills
- Experience with infrastructure as code tools like Terraform or Cloud Deployment Manager
- Experience with data and AI agent/model lineage
- Knowledge of AI agent/model monitoring, drift detection, and observability
- Understanding of scalable AI agent deployment patterns
- Strong understanding of GCP security principles, IAM, and network security
- Experience with feature stores (Good to Have)
- Familiarity with MLflow or similar experiment tracking tools (Good to Have)
- Experience with cost optimization on cloud platforms (Good to Have)
Job title
Job type
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Salary
Not specified
Degree requirement
Tech skills
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