Sr AI ML Architect - GCP

Position Overview:

We are seeking a highly experienced Senior AI/ML Architect with a proven track record of designing and implementing enterprise-scale AI/ML solutions on the Google Cloud Platform (GCP) . This role requires deep technical expertise in cloud-native AI/ML services, combined with the ability to define architectural standards, guide cross-functional teams, and align advanced AI initiatives with business objectives. The successful candidate will shape our AI/ML strategy, drive innovation using Vertex AI and Generative AI services , and ensure solutions are scalable, secure, and cost-efficient.

Key Responsibilities:

• Lead the end-to-end design and architecture of AI/ML platforms and solutions within GCP .
• Define and implement enterprise standards for AI/ML architectures, data governance, and MLOps .
• Partner with business leaders, data scientists, and engineers to translate business goals into AI-driven solutions.
• Architect scalable ML platforms using Vertex AI, AI Platform, BigQuery ML, and Dataplex .
• Oversee the design of end-to-end ML pipelines , ensuring modularity, automation, monitoring, and cost optimization.
• Evaluate and recommend AI/ML frameworks, tools, and GCP services to ensure alignment with evolving enterprise needs.
• Drive adoption of Generative AI, NLP, computer vision, and advanced ML techniques where applicable.
• Provide architectural guidance on data ingestion, feature engineering, training, deployment, and monitoring pipelines.
• Collaborate with security and compliance teams to ensure solutions adhere to governance and regulatory requirements.
• Mentor engineering teams and foster best practices in AI/ML development, deployment, and cloud adoption .

Required Skills:

• 12+ years of experience in data, AI/ML, or cloud solution architecture, with at least 5 years in GCP-based AI/ML solutions .
• Expertise in GCP AI/ML services , including Vertex AI, BigQuery ML, Dataplex, Pub/Sub, Dataflow, and Cloud Functions .
• Strong experience in MLOps strategy and implementation (CI/CD for ML, model monitoring, retraining pipelines).
• Proficiency in Python and major ML frameworks (TensorFlow, PyTorch, Scikit-learn).
• Deep understanding of machine learning and deep learning architectures , including NLP, computer vision, and recommendation systems.
• Strong knowledge of data architecture and governance principles in cloud environments.
• Familiarity with Kubernetes (GKE) for containerized ML deployments.
• Ability to evaluate trade-offs between performance, scalability, cost, and complexity in enterprise AI solutions.
• Strong knowledge of SQL and data modeling for large-scale analytics platforms.