Design, develop, test, deploy, and support scalable, secure, and maintainable software and ML solutions, including backend services, APIs, cloud-native applications, data integrations, and ML-enabled services supporting Forecasting and AI products.
Translate business and functional requirements into technical designs and high-quality implementations, contributing across the full Software Development Lifecycle (SDLC).
Build and maintain data ingestion, transformation, preprocessing, feature engineering, and post-processing pipelines to support ML model training, experimentation, and inference.
Partner with Data Scientists and Data Engineers to translate model requirements into production-ready engineering solutions and ensure high-quality, reliable data is available for model development and execution.
Support the end-to-end ML lifecycle, including experimentation, model registration and versioning, testing, deployment, orchestration, inference, and ongoing model management using enterprise AI/ML platforms.
Develop and maintain CI/CD and automation pipelines for software, data pipelines, and ML models to enable reliable and repeatable testing and deployments.
Implement monitoring and observability for applications, data pipelines, and ML models, including system health, data quality, model performance and drift, and operational metrics.
Optimize software, ML workloads, and data pipelines for performance, scalability, reliability, and cost efficiency across cloud and enterprise platforms.
Apply engineering best practices including secure coding, code reviews, automated testing, documentation, performance optimization, data governance, security, and compliance.
Support production systems through monitoring, incident resolution, root-cause analysis, troubleshooting, and continuous reliability improvements.
Collaborate with Product, Data Science, Data Engineering, Architecture, Security, Infrastructure, and business teams to deliver reliable Forecasting and AI capabilities that support business decision-making.
Participate in technical design discussions and code reviews, contribute to engineering standards, share knowledge with team members, and evaluate modern technologies, AI-enabled development tools, and automation to continuously improve software quality and engineering productivity.