• Design, Build, and Maintain Scalable ML Infrastructure: Lead the design and development of scalable machine learning infrastructure on AWS, utilizing services like AWS Sagemaker for efficient model training and deployment. • Collaborate with Product Teams: Work closely with product teams to develop MVPs for AI-driven features, ensuring quick iterations and market testing to refine solutions effectively. • Develop Monitoring & Alerting Frameworks: Create and enhance monitoring and alerting systems for machine learning models to ensure high performance, reliability, and minimal downtime. • Support Marketing Team’s AI Utilization with an eye on Cross-Departmental impact: Enable various departments within the organization to leverage AI/ML models, including cutting-edge Generative AI solutions, for different use cases. • Provide Production Support: Offer expertise in debugging and resolving issues related to machine learning models in production, participating in on-call rotations for operational troubleshooting and incident resolution. • Scale ML Architecture: Design and scale machine learning architecture to support rapid user growth, leveraging deep knowledge of AWS and ML best practices to ensure robustness and efficiency. • Mentor and Elevate Team Skills: Conduct code reviews, mentor team members, and elevate overall team capabilities through knowledge sharing and collaboration. • Stay Ahead of the Curve: Stay updated with the latest advancements in machine learning technologies and AWS services, driving the adoption of cutting-edge solutions to maintain a competitive edge.

• Lead the design, development, and ownership of AI-powered internal platforms and systems, including initiatives built from the ground up with a focus on Marketing. • Own end-to-end delivery of complex projects, from problem discovery and system design through production rollout and iteration. • Design and implement AI-driven features, intelligent automation, and orchestration systems that enhance customer experiences and compound productivity across teams. • Embed AI capabilities into customer-facing products, internal tools, and operational workflows, ensuring solutions integrate naturally and deliver measurable value. • Build full-stack systems spanning backend services, APIs, data layers, and frontend interfaces. • Translate ambiguous business and operational problems into scalable, maintainable, AI-enabled technical solutions.

• Contribute to the design and development of AI-powered platforms and systems, working alongside senior engineers on complex initiatives. • Own end-to-end delivery of complex projects, from problem discovery and system design through production rollout and iteration. • Build AI-driven features, intelligent automation, and agentic systems that enhance customer experiences and improve team productivity. • Embed AI capabilities into existing team workflows, products, and processes, ensuring solutions integrate naturally. • Integrate AI capabilities into customer-facing products, internal tools, and operational workflows. • Build full-stack components spanning backend services, APIs, data layers, and frontend interfaces. • Translate business and operational problems into maintainable, AI-enabled technical solutions with guidance from senior team members. • Apply systems thinking to ensure reliability, performance, and security across platforms. • Collaborate with engineers, teams, and stakeholders across the company to understand requirements and deliver impactful solutions. • Participate in production support and incident response for team-owned systems, learning operational best practices. • Engage in code reviews and knowledge sharing to continuously improve skills and contribute to team growth.

• Design, Build, and Maintain Scalable ML Infrastructure: Lead the design and development of scalable machine learning infrastructure on AWS, utilizing services like AWS Sagemaker for efficient model training and deployment. • Collaborate with Product Teams: Work closely with product teams to develop MVPs for AI-driven features, ensuring quick iterations and market testing to refine solutions effectively. • Develop Monitoring & Alerting Frameworks: Create and enhance monitoring and alerting systems for machine learning models to ensure high performance, reliability, and minimal downtime. • Support Marketing Team’s AI Utilization with an eye on Cross-Departmental impact: Enable various departments within the organization to leverage AI/ML models, including cutting-edge Generative AI solutions, for different use cases. • Provide Production Support: Offer expertise in debugging and resolving issues related to machine learning models in production, participating in on-call rotations for operational troubleshooting and incident resolution. • Scale ML Architecture: Design and scale machine learning architecture to support rapid user growth, leveraging deep knowledge of AWS and ML best practices to ensure robustness and efficiency. • Mentor and Elevate Team Skills: Conduct code reviews, mentor team members, and elevate overall team capabilities through knowledge sharing and collaboration. • Stay Ahead of the Curve: Stay updated with the latest advancements in machine learning technologies and AWS services, driving the adoption of cutting-edge solutions to maintain a competitive edge.