• Collaborate closely with business stakeholders and other engineers to deliver impactful solutions. • Integrate services and product features with databases (Cloud SQL, Redis) and messaging queues (Pub/Sub). • Contribute to the development of our MLOps tools for ML models. • Build and optimize large-scale data processing pipelines for feature extraction and data transformation using BigQuery and Dataflow. • Train, evaluate, deploy, and monitor machine learning models on Vertex AI to improve bidding performance. • Stay fast and focused, balancing speed, performance, and scalability to deliver impact.

• Own the ideation and execution of high-impact projects that directly influence the user experience and business outcomes • Flesh out and evolve the Conversational AI Engineering roadmap alongside product and technical leadership • Incept projects: identify opportunities, design solutions, and lead implementation • Guide integration efforts with external agencies and insurance carriers • Normalize, rank, and evaluate results using ML-based predictions to determine optimal user paths • Work closely with our Data Science team to leverage MLMs in tools like DataRobot for retention forecasting and decision logic • Ensure scalability and performance across the platform as we continue to grow

• Implement AI-driven operations, including log analysis, automated infrastructure updates, and predictive scaling alerts • Benchmark cloud and edge services to improve performance and scalability • Build self-healing infrastructure pipelines that demonstrate advanced AI capabilities • Design, automate, and manage infrastructure using Terraform and AWS services (ECS/Fargate, RDS, S3, IAM) • Build and maintain CI/CD pipelines using GitHub Actions for efficient deployments • Implement and manage observability tools such as Prometheus, Grafana, OpenTelemetry, and Sentry • Handle containerization using Docker and troubleshoot performance issues under load • Collaborate with backend teams to ensure low-latency, scalable, and cost-effective services • Progress into a Staff Platform Engineer or Lead SRE role with end-to-end platform ownership • Contribute to building scalable deployment frameworks for enterprise use cases • Help define best practices for AI-driven DevOps and infrastructure management.

• Design, maintain and tune high performance code. • Apply appropriate approach, best-practice and correct separation of presentation code from business logic. • Implement and support build processes and test environments. • Provide project and task estimates. • Responsible for developing, enhancing, modifying and/or maintaining applications. • Deeply understand requirements and expertly handle Analysis, Design, Coding, Testing and Quality processes.