Software Engineer_Java

• The primary purpose of this role is to design, build, and operate scalable software and machine learning solutions that enable Data & AI-driven products. The role combines strong software engineering fundamentals with ML/MLOps, data engineering, cloud-native development, and AI-enabled engineering practices.
• The engineer will contribute across the full Software Development Lifecycle (SDLC), from solution design and development through testing, deployment, production support, and continuous improvement. Key responsibilities include building robust data and feature pipelines, productionizing and scaling ML models, optimizing model training and inference, and implementing model monitoring and lifecycle management.
• The ideal candidate will apply software engineering rigor to ML workloads, leverage AI/ML platforms and automation to accelerate experimentation and deployment, and partner with Data Science, Data Engineering, Product, and Engineering teams to deliver reliable solutions that enable faster, data-driven business decisions.

Roles & Responsibilities

•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.