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.
