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 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.

• 2-5 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.
• Hand on experience in Machine Learning: Supervised and unsupervised learning, model building, evaluation, and optimizations.
• Hand on experience in Deep Learning: Neural networks, CNNs, RNNs, LSTMs, transformers, and LLMs ; LLM fine-tuning and deployment
• Completed projects in Natural Language Processing (NLP): Text pre-processing, tokenization, embeddings, sentiment analysis, named entity recognition (NER), anomaly detection
• Frameworks and Libraries: PyTorch, Keras, Scikit-learn, Hugging Face Transformers.
• Worked in Cloud Platforms: Databricks(AI-ML) - preferred / AWS (SageMaker)
• MLOps/LLMOps
• 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 teams.
• Good to have :
  
  
  • Data Processing: Pandas, NumPy, Spark.
  • DevOps: Docker, Kubernetes,DVC(Data Version control)/model monitoring and versioning.

• 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.