GCP AI Architect

Job Description About the Role Experienced Google Cloud Platform (GCP) Cloud Architect to design and oversee the implementation of a scalable, secure, and cost-efficient cloud architecture to support AI/ML platforms built primarily on PaaS and SaaS services in GCP. The Cloud Architect will work closely with AI developers, data scientists, and platform engineers to define the cloud foundation, validate service components, and ensure compliance with enterprise standards and best practices. This role will be pivotal in shaping the AI infrastructure strategy, ensuring that the platform can support advanced machine learning workloads, data pipelines, and production AI/ML services on GCP. Key Responsibilities Architecture Design o Design and validate GCP infrastructure to host AI/ML platforms leveraging GCP PaaS/SaaS services (e.g., Vertex AI, BigQuery, Pub/Sub, Dataflow, Dataproc, AI APIs, Cloud Run, GKE Autopilot, Cloud Functions). o Define Landing Zone architecture (IAM, networking, security, monitoring, logging, billing, policies). o Ensure high availability, scalability, and security in line with enterprise and compliance requirements. Service Validation & Governance o Evaluate, recommend, and validate GCP service components and versions required for AI workloads. o Establish reference architectures and blueprints for data ingestion, model training, model deployment, and MLOps. o Define best practices for cost optimization, performance, and resilience. Collaboration with Terraform Developers o Validate IaC (Infrastructure-as-Code) designs for compliance with security,and operational standards. o Collaborate with stakeholders including Data Scientists, ML Engineers, Platform Engineers, and Security Teams. Technical Skills & Experience Proven experience (8+ years) as a Cloud Architect or similar role, with strong expertise in Google Cloud Platform. Experience with GenAI, LLMs, RAG, vector databases and conversational bots Experience with Playbooks , Vertex AI Exposure to ADK (hands-on) and Voice AI Exposure to LangChain and/or LangGraph Hands-on experience designing and implementing AI/ML platforms on GCP using services such as: o Vertex AI (training, deployment, pipelines, feature store). o BigQuery, Dataflow, Pub/Sub, Dataproc, Cloud Storage. o Cloud Run, GKE (Autopilot/Standard), Cloud Functions. Strong knowledge of MLOps and data engineering practices. Familiarity with CI/CD pipelines for ML (e.g., Cloud Build, GitHub Actions,Tekton). Knowledge of Kubernetes, Kubeflow, MLflow, or similar orchestration tools. Experience in Developing, fine-tuning, and deploying Generative AI models for real-world applications. Experience on NLP tasks such as text summarization, language translation,sentiment analysis, and question answering. Building and optimizing ML and DL pipelines for AI-based solutions. Designing and implementing Audio/Visual Speech-to-Text systems for transcription and speech recognition applications. Exploring and integrating advanced speech processing frameworks for tasks like speech synthesis and speaker identification. Collaborating with cross-functional teams to integrate AI models into projects and applications. Staying updated on the latest advancements in AI, speech technology, and applying them to enhance existing solutions. Experience in writing clean and efficient Python code for AI model development and deployment. Documenting processes, experiments, and results for knowledge sharing Preferred Qualifications GCP Professional Cloud Architect and/or Professional Machine Learning Engineer certifications. Experience in multi-cloud or hybrid architectures. Exposure to Agentic AI. Soft Skills & Functional Expertise Strong communication and collaboration skills to engage both business and technical stakeholders. Strategic thinker capable of balancing technical depth with business context. Proven ability to translate business needs into scalable data architecture solutions.