Our Opportunity: 

At Chewy, data is at the heart of our decision-making, and our HR organization is no exception. We are seeking a dedicated Software Engineer II to join our People Analytics team as the first dedicated Software Engineer focused on building scalable ML Ops pipelines in the HR space. This role represents a unique opportunity to pioneer brand new machine learning infrastructure and deployment processes in a greenfield environment.

As a key contributor, you’ll design and implement innovative solutions that enable HR to demonstrate artificial intelligence and machine learning at scale. Collaborating closely with data scientists, data engineers, and analysts, you’ll bridge the gap between research and production, ensuring seamless integration and impactful outcomes. Your work will directly enhance Chewy’s ability to optimize the employee experience, improve operational efficiency, and empower strategic HR decisions through advanced data capabilities.

This role offers an unparalleled chance to make your mark on a growing organization while working on brand new tools and technologies. You’ll have the freedom to experiment, innovate, and shape the future of AI driven HR solutions at Chewy. If you’re excited about solving complex challenges and building systems that make a difference, this is the role for you!

Key Responsibilities:

• Build and maintain scalable ML Ops pipelines, including model versioning, testing, and monitoring.
• Design containerized solutions using Docker and Kubernetes for efficient deployment and scaling.
• Implement Infrastructure as Code (IaC) solutions using Terraform or CloudFormation to streamline infrastructure management.
• Develop and handle CI/CD workflows tailored for AI models and data pipelines to enable rapid iteration and deployment.
• Optimize AWS Service based cloud applications to with achieve cost-efficiency and performance.
• Collaborate with applied scientists and data engineers to ensure seamless integration between research prototypes and production systems.
• Build robust ETL/ELT pipelines to process structured and unstructured HR data using tools like Apache Airflow or AWS Glue.
• Ensure quality through automated testing frameworks and implement quality assurance processes for machine learning pipelines.
• Translate complex technical requirements into impactful solutions aligned with HR and business strategies.

Qualifications:

• 5+ years of experience with containerization and orchestration tools like Docker and Kubernetes.
• Proficiency in Infrastructure as Code (IaC) tools, including Terraform and CloudFormation.
• Expertise in Continuous Integration and Continuous Deployment (CI/CD) systems and workflows, particularly with Github Actions and Jenkins.
• Familiarity and tried work experience integrating web-services and applications into existing private networks.
• Strong proficiency in Python and SQL, with experience in libraries like pandas, scikit-learn, and PyTorch or TensorFlow.
• Experience in deploying machine learning models in production environments using tools like SageMaker or MLflow.
• Excellent problem-solving skills and approach to work effectively in multi-functional teams.

Preferred Qualifications:

• Experience with monitoring and observability tools such as Prometheus, Grafana, or similar.
• Knowledge of security standard processes in cloud and ML deployments.
• Strong understanding of data engineering principles and tools like Apache Spark or Kafka.
• Familiarity with ML Ops frameworks like Kubeflow, MLflow, or similar platforms.
• Experience in creating cost-optimized and scalable solutions for machine learning workflows in the cloud.
• Ability to effectively communicate technical concepts to non-technical collaborators.