Software Engineer II,ITC

Who You?ll Work With You?ll be joining a dynamic, fast-paced Global FPE (Foundational Platforms Engineering) team within Nike. Our mission is to build and scale world-class cloud-native platforms, enabling Nike?s data-driven decision-making and intelligent automation capabilities. This role sits right into AI-driven innovation helping to drive cutting-edge advancements in both analytics and intelligent automation. Collaboration and creativity are at our core, and we are passionate about leveraging cloud-scale data platforms and AI-powered automation to transform business operations. Who We Are Looking For We are seeking a Software Engineer II who brings deep expertise in Databricks, AWS Services, Cloud Platforms, and AI-driven automation. You are someone who thrives in building scalable, high-performance data platforms to improve efficiency, insights, and user experience. Key Skills & Traits 2+ years of production experience in AI/ML model development, deployment, and maintenance Proven expertise with Large Language Models (LLMs) and NLP tasks Strong background in data science and cloud-based AI/ML services (Databricks preferred) Expertise in MLOps/LLMOps for scalable model deployment and management Advanced programming skills in Python, SQL, and automation frameworks Worked in Cloud Platforms: Databricks(AI-ML) MLOps/LLMOps and MLFlow Passion for leveraging AI to enhance automation, efficiency, and analytics Strong collaboration, problem-solving, and leadership skills, with the ability to drive initiatives across multiple team Good To Have Data Processing: Pandas, NumPy, Spark. DevOps: Docker, Kubernetes,DVC(Data Version control)/model monitoring and versioning. What You?ll Work On As a Software Engineer II, you will play a crucial role in shaping, modernizing, and scaling by helping driving AI adoption and automation. Core AI/ML Engineer Responsibilities Develop end-to-end ML pipelines with focus on production reliability. Implement robust testing and validation frameworks for ML models. Establish best practices for model versioning and reproducibility. Build and optimize production-grade ML models . Develop custom NLP solutions for text analysis and processing. Create automated model evaluation and optimization pipelines. Manage ML infrastructure on Databricks cloud platform. Ensure scalability and cost optimization of ML deployments. Maintain data quality and pipeline efficiency. Maintain security and compliance implementations for ML systems. Evangelize AI adoption, helping Nike teams unlock new automation opportunities.