Gen AI Architect- Python, Google ADK - Manager

Responsibilities

• Leading client-facing Gen AI architecture workstreams focused on Python-based development and Google ADK implementation
• Designing and building scalable data and AI solutions that support data pipelines, integration, transformation, and downstream analytics use cases
• Translating business needs into technical architecture, solution patterns, and implementation plans for data and AI initiatives
• Guiding teams in the selection and application of data engineering tools, cloud services, and orchestration frameworks such as Apache Airflow and Databricks
• Overseeing the development of data models, data quality controls, and validation approaches across complex information flows
• Reviewing technical deliverables for alignment with architecture standards, security requirements, and platform design principles
• Coaching team members on solution design, coding patterns, performance tuning, and delivery methods across project workstreams
• Identifying opportunities to improve data infrastructure, automation, and AI-enabled delivery methods across client engagements
• Managing project scope, timelines, risks, and stakeholder expectations while supporting multiple client needs
• Applying knowledge of data governance, privacy, and anonymization practices in the design of AI and data solutions
• What You Must Have
• At least a Bachelor's degree
• At least 8+ years of experience
• Oral and written proficiency in English required
• What Sets You Apart
• Preference for at least one of the following fields of study: Management Information Systems, Computer and Information Science, Systems Engineering, Electrical Engineering, Chemical Engineering, Industrial Engineering, Mathematics, Statistics, Mathematical Statistics
• Building GenAI solutions using Python and Google ADK
• Leading client-facing data and AI engineering workstreams
• Managing project planning, budgeting, execution, and completion
• Mentoring teams while reviewing deliverables and timelines
• Applying AWS, Databricks, or Snowflake in delivery
• Translating system interactions into practical architecture decisions