Design and implement comprehensive end-to-end AI/ML architectures, including data ingestion, feature engineering, model deployment, and monitoring.
Partner with business and technical teams to translate use cases into scalable AI solutions, supporting enterprise goals.
Establish and promote best practices for AI adoption, including reusable patterns, accelerators, and governance frameworks.
Collaborate with data architecture teams to ensure data quality, lineage, and governance for AI workloads.
Provide technical leadership, mentorship, and guidance to data scientists, ML engineers, and platform teams.
What's Needed?
8+ years of experience in data, analytics, or platform architecture roles with a focus on AI/ML solutions at scale.
Proven expertise in deploying AI models into production environments and managing end-to-end AI architecture.
Strong knowledge of machine learning, deep learning, and Generative AI concepts, along with Python, SQL, and ML frameworks like TensorFlow and PyTorch.
Experience with cloud AI platforms (Azure, AWS, GCP), MLOps tools, and data engineering platforms such as DataStage or Spark.
Excellent communication skills, with the ability to engage with leadership and manage stakeholder expectations effectively