Cloud Architect

Responsibilities

• Design AI/ML and GenAI solution architectures on GCP using Vertex AI , BigQuery, Cloud Run, Cloud Functions, and GCS.

• Set up and configure Vertex AI components (Workbench, Pipelines, Model Registry, Feature Store, Vector Search, Model Deployment).

• Work with teams to design ML workflows, including data ingestion, preprocessing, model development, deployment, and monitoring.

• Support the build of prompt-based, RAG, and LLM-driven applications using Gemini and Vertex AI Generative AI features.

• Collaborate with data engineering teams to integrate models with BigQuery and other GCP data services.

• Build MLOps pipelines using Cloud Build, Cloud Deploy, GitHub Actions, and Terraform.

• Prepare architecture diagrams, solution documents, and design specifications.

• Participate in client workshops to understand use cases and recommend solution approaches.

• Review solution designs from engineering teams and ensure alignment with architecture standards.

• Support cost estimation, performance optimisation, and deployment planning.

• Ensure adherence to security, IAM, network, and governance guidelines on GCP.

• Provide technical guidance and mentorship to development and data teams.

• Support pre-sales activities when needed (solutioning, estimation, scoping).

Required Skills

• Strong understanding of Google Cloud Platform architecture: IAM, VPC, networking, compute, logging, monitoring .

• Hands-on experience with Vertex AI , including model training, deployment, tuning, and pipelines.

• Experience implementing GenAI/LLM -based solutions (Gemini, RAG, embeddings, vector stores).

• Knowledge of core machine learning concepts and ability to work with data scientists/ML engineers.

• Proficiency with Python, ML frameworks (TensorFlow/PyTorch), and REST APIs.

• Practical experience with Terraform, CI/CD, and automation on GCP.

• Good understanding of BigQuery, Dataflow/Beam basics, and data modelling.

• Ability to produce clean and clear architecture diagrams and documentation.

• Strong communication skills and ability to work with clients and internal teams.

Good-to-Have Skills

• Experience in telecom, Domain

• Experience building ML evaluation, monitoring, and drift detection frameworks.

• GCP certifications (Professional Cloud Architect / ML Engineer / Data Engineer).