SENIOR SOFTWARE ENGINEER - ML OPS 4 - 7 Years

As a Senior Software Engineer- MLOps, you'll architect and manage ML deployment pipelines, ensuring models are delivered in scalable, secure and production-grade environments. You will implement CI/CD, container orchestration, monitoring and governance frameworks, serving as a critical link between ML engineering and enterprise infrastructure aligned with HD Supply's tech stack.

JOB REQUIREMENTS

Education and Certifications

. Bachelor's or Master's degree in computer science, software engineering, or related fields

. Certifications in DevOps, Kubernetes, GCP, or MLOps preferred

Required Experience

. 4-7 years in ML/DevOps roles, building CI/CD pipelines and deployment frameworks for ML applications

. Experience implementing CI/CD pipelines for ML artifacts and model packaging

. Proficient in containerization (Docker), orchestration (Kubernetes / EKS / GKE), and Airflow/Prefect pipelines

. Hands-on support for production ML deployments: caching, load balancing, version rollback

Essential skills

. Experience building automated ML pipelines using CI/CD tools such as Jenkins, GitLab CI, Azure DevOps

. Proficiency in Linux administration, containerization (Docker) and Kubernetes orchestration

. Strong hands-on experience with Google Cloud Platform (GCP)

. Experience working with Vertex AI for scalable ML pipeline deployment

. Knowledge of monitoring, logging and alerting frameworks (Prometheus, Grafana, ELK stack)

. Proficiency in Python/Bash scripting and automated testing frameworks

. Familiarity with deploying ML models as scalable API services (Seldon, KFServing)

Desired skills

. Familiarity with Google Vertex AI pipeline

. Understanding of Snowflake architecture and its integration points

. Experience with feature store implementation, MLOps platform architecture

. Certifications in cloud-native technologies, MLOps, or Kubernetes

. Understanding of security/risk controls around ML deployments

. Familiarity with machine learning, model development

. Familiarity with machine learning test automation and continuous validation frameworks

. Monitoring logs by enabling or setting up log analytics dashboard

ROLES & RESPONSIBILITIES

Delivery and Execution

. Define the MLOps pipeline architecture: version control, model validation, deployment, rollback mechanisms

. Work closely with ML engineers to design scalable, reliable system-level integration plans

. Architect model lifecycle flows consistent with enterprise standards and service-level requirements

. Build and maintain CI/CD pipelines for ML workflows, including model packaging, testing, serving

. Deploy containers and microservices to Kubernetes or managed cloud services

. Implement monitoring solutions to track model performance, drift, and system health

Support and Enablement

. Automate operational tasks such as deployments, scaling, canary releases, and job scheduling via Airflow

. Document system configurations, incident protocols, and deployment playbooks

. Conduct post-mortems and root cause analyses for platform incidents