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.

Key Skills & Traits:

• 1+ 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 teams.

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, 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