Google AI Architect

As a Google AI Architect, your role will involve leading the design, development, and deployment of advanced AI/ML solutions utilizing Google Cloud Platform (GCP) and Vertex AI. You should have a strong background in enterprise architecture and cloud-native AI/ML systems, with proven experience in driving end-to-end AI transformation initiatives across various industries. Key Responsibilities: - Define and drive AI/ML architecture strategies using GCP services such as Vertex AI, BigQuery ML, TensorFlow, and Generative AI Studio. - Lead the design and implementation of AI solutions across enterprise applications, data platforms, and customer-facing products. - Architect and deploy scalable, secure, and cost-optimized AI/ML solutions in both cloud and hybrid environments. - Collaborate with data scientists, ML engineers, and business stakeholders to develop AI-driven solutions for business challenges. - Evaluate and integrate GenAI, LLMs, Agentic AI, and conversational AI platforms for enterprise use cases. - Provide technical leadership and governance, ensuring best practices in AI architecture, MLOps, and responsible AI. - Mentor engineering teams on AI/ML frameworks, pipelines, and cloud-native development. - Stay updated on emerging AI trends, Google Cloud innovations, and industry practices to influence strategic roadmaps. Qualifications Required: - 15-18 years of overall IT experience, with a minimum of 6-8 years in AI/ML architecture and solutioning. - Deep expertise in Google Cloud Platform (GCP) services including Vertex AI, BigQuery, AI/ML APIs, TensorFlow, AutoML, and Dataflow. - Strong knowledge of Generative AI, LLMs, prompt engineering, RAG, and Agentic AI systems. - Hands-on experience in MLOps, CI/CD for ML, model governance, and monitoring. - Proven track record in designing and deploying enterprise-scale AI/ML solutions. - Proficiency in Python, Java, or Scala with a strong understanding of ML frameworks like TensorFlow, PyTorch, and Scikit-learn. - Solid grasp of data engineering, data lakes, ETL pipelines, and API-driven integrations. - Expertise in enterprise architecture frameworks and microservices/cloud-native patterns. - Excellent leadership, communication, and stakeholder management skills. Additional Company Details: - Desired Skills include Google Cloud Professional Machine Learning Engineer or Google Cloud Professional Cloud Architect certification. - Experience with multi-cloud and hybrid AI strategies (AWS, Azure) is a plus. - Exposure to AI ethics, explainability, and compliance frameworks is desirable. - Industry expertise in banking, retail, healthcare, or telecom is considered beneficial for this role.,