Integrate the digital stack. Connect models and AI services with enterprise platforms, data sources, workflow systems, user interfaces and upstream or downstream applications.
Mentor junior AI engineers, set best practices for prompt engineering, RAG pipelines, and agent evaluation, and help build reusable AI infrastructure for the broader team.
Technical Leadership & Solution Ownership: Lead the technical direction of AI initiatives by driving architecture decisions, establishing best practices, mentoring team members, and guiding the end-to-end delivery of scalable AI solutions.
Mentor junior AI engineers, set best practices for prompt engineering, RAG pipelines, and agent evaluation, and help build reusable AI infrastructure for the broader team.
Technical Leadership & Solution Ownership: Lead the technical direction of AI initiatives by driving architecture decisions, establishing best practices, mentoring team members, and guiding the end-to-end delivery of scalable AI solutions.
Collaborate and iterate cross-functionally: Work in an agile team with product managers, designers, and data scientists to turn requirements into well-designed APIs, and refine features iteratively as models and interfaces evolve.
Technical leadership & mentoring. Mentor and support other engineers, share knowledge across the team, and help grow AI/ML capability. Set and uphold engineering best practices through code and architecture review.
Technical Leadership & Solution Ownership: Lead the technical direction of AI initiatives by driving architecture decisions, establishing best practices, mentoring team members, and guiding the end-to-end delivery of scalable AI solutions.
Act as the primary technical point of contact for government and enterprise clients, leading technical workshops, solution walkthroughs, and architecture presentations
Collaborate with data scientists to improve feature selection, feature engineering, and enable end-to-end AI workflows from model training to deployment and monitoring.
Follow and improve feature development cycles, software architecture patterns, coding standards, and DevOps/MLOps best practices across the engineering team.
Re-architect and develop our existing system to support conversion of complex business and retail-specific requirements into scalable and reusable software components.