Project Role :
Application ArchitectProject Role Description :
Provide functional and/or technical expertise to plan, analyze, define and support the delivery of future functional and technical capabilities for an application or group of applications. Assist in facilitating impact assessment efforts and in producing and reviewing estimates for client work requests.Must have skills :
Google Cloud Platform ArchitectureGood to have skills :
NAMinimum 5 Year(s) Of Experience Is Required
Educational Qualification :
15 years full time educationRole Summary The GCP Architect for AI/ML and Agentic Solutions will lead the design, implementation, and optimization of scalable AI/ML platforms and multi-agent architectures on Google Cloud Platform (GCP). This role bridges AI innovation and cloud engineering, ensuring robust infrastructure, secure data flow, and intelligent orchestration to enable next-generation autonomous and agentic AI systems. The architect will collaborate with data scientists, MLOps engineers, and solution architects to create a cohesive platform that supports model development, deployment, monitoring, and agentic collaboration leveraging GCP-native and open-source components. Key Responsibilities Architect and design scalable AI/ML platforms leveraging GCP services such as Vertex AI, Dataflow, BigQuery, Pub/Sub, Cloud Storage, Cloud Run, and GKE. Define and implement multi-agent system architectures using frameworks such as Agent Developer Kit (ADK), LangChain, or Vertex AI Agents. Design end-to-end pipelines for data ingestion, preprocessing, training, inference, and feedback loops. Implement MLOps frameworks for model versioning, deployment, retraining, and observability. Define reference architectures and reusable templates for AI/ML and agentic workloads. Ensure data security, governance, and compliance across model development and agent interactions. Integrate LLMs, knowledge bases, and reasoning components for adaptive and autonomous decision systems. Collaborate with development and DevOps teams to operationalize AI models using CI/CD and IaC best practices. Provide technical leadership, architecture governance, and design reviews for AI and platform initiatives. Stay updated with emerging GCP AI/ML services, agentic frameworks, and industry innovations to drive continuous improvement. Technical Skills Strong expertise in Google Cloud services: Vertex AI, BigQuery, Pub/Sub, Dataflow, Dataproc, Cloud Functions, Cloud Run, GKE, and IAM. Experience designing and implementing AI/ML pipelines (training, inference, retraining) using Vertex AI Pipelines and MLOps frameworks. Understanding of agent-based architectures, including autonomous orchestration, reasoning frameworks, and contextual memory systems. Proficiency in Python, TensorFlow, PyTorch, and MLflow, and experience integrating models into production systems. Knowledge of data engineering patterns and ETL orchestration with Dataflow or Apache Beam. Familiarity with IaC tools (Terraform, Deployment Manager) and DevOps/MLOps automation. Experience with API design and integration using Apigee or Cloud Endpoints for serving AI APIs and agent interactions. Strong grasp of networking, IAM, VPC design, and data protection best practices on GCP. Professional and Behavioral Skills Excellent communication and presentation skills, able to translate complex AI architectures into business context. Strong collaboration and stakeholder engagement across engineering, AI research, and product management teams. Strategic thinking with the ability to balance innovation, feasibility, and scalability. Exceptional problem-solving, analytical, and conceptual design abilities. Proven experience mentoring engineers and guiding teams in AI/ML adoption and architecture best practices. Strong documentation and governance mindset — producing architecture blueprints, design patterns, and decision records. Preferred Qualifications Google Cloud Professional Cloud Architect and/or Professional Machine Learning Engineer certification. Experience with multi-agent orchestration frameworks (e.g., ADK, CrewAI, AutoGen, or LangGraph). Exposure to Knowledge Graphs, vector databases, and retrieval-augmented generation (RAG) architectures. Understanding of AI governance, model explainability, and compliance frameworks. Familiarity with Anthos, hybrid/multi-cloud AI deployment, and federated learning setups. Prior experience designing AI Platforms-as-a-Service or reusable Agentic AI frameworks. Education & Experience Bachelor’s or Master’s degree in Computer Science, Data Engineering, or related field. 8–12 years of total experience, with at least 4+ years in GCP architecture and 2+ years in AI/ML solutioning.
