Machine Learning Engineer

Optomi; United States; On-site; USD 120,000 - 180,000; Full time

Job summary

Optomi is seeking an ML Engineer to migrate AI/ML models from analytics to full-scale IT environments in a retail setting, designing scalable MLOps pipelines and ensuring security and reliability across platforms.

You will partner with data scientists and data engineers to productionize pricing models, develop data workflows with Python, Airflow/Composer, Vertex AI, and Kubernetes, and implement monitoring, logging, and retraining processes.

Qualifications

• Proficient Python programming and software engineering skills.
• Hands-on MLOps experience with production ML pipelines.
• Experience building CI/CD pipelines and using Git/GitHub.
• Strong understanding of ML lifecycle management, deployment, monitoring, and automation.
• Experience with REST APIs/microservices and Docker containerization.
• Ability to collaborate with Data Science and Data Engineering teams.
• Experience with GCP, Vertex AI, BigQuery, Cloud Storage, and Composer/Airflow.

Responsibilities

• Design, build, and maintain scalable MLOps pipelines for ML model development, deployment, and monitoring.
• Develop CI/CD pipelines to automate model and application deployment across environments.
• Build and manage data and ML workflows using Python, Airflow/Composer, Git, and cloud platforms.
• Deploy and manage ML models using Vertex AI or equivalent platforms.
• Implement model monitoring, performance tracking, logging, alerting, and retraining workflows.
• Work closely with Data Scientists, Data Engineers, and Software Engineers to productionize ML models.
• Develop APIs and services to integrate ML models with downstream applications.
• Ensure solutions are scalable, reliable, secure, and maintainable.
• Troubleshoot production issues and optimize ML pipelines for performance and reliability.
• Follow software engineering best practices including code reviews, testing, documentation, and version control.

Skills

Python

MLOps

CI/CD

Model lifecycle

REST APIs

Docker

GCP Vertex AI

Tools

Vertex AI

BigQuery

Cloud Storage

Composer/Airflow

Docker

Git/GitHub

Job description

Optomi, in partnership with a leading retail company, is seeking a ML Engineer to join their team! This role is crucial for supporting the migration of AI and ML models from the analytics phase to a full-scale IT environment. The successful candidate will work closely with data science partners to productionize pricing models, ensuring they are scalable, secure, and integrated with IT systems. This involves building data engineering pipelines and utilizing a tech stack including Airflow, Vertex AI, and Kubernetes. The position requires a strong foundation in SQL and Python, with a collaborative mindset to work across functions.

Required Qualifications:

• Strong Python programming and software engineering skills.
• Hands-on MLOps experience with production ML pipelines.
• Experience building CI/CD pipelines and working with Git/GitHub.
• Strong understanding of ML model lifecycle management, deployment, monitoring, and automation.
• Experience with REST APIs/microservices and containerization such as Docker.
• Ability to work effectively with Data Science and Data Engineering teams.
• Experience with GCP, preferably Vertex AI, BigQuery, Cloud Storage, and Composer/Airflow.

Responsibilities:

• Design, build, and maintain scalable MLOps pipelines for ML model development, deployment, and monitoring.
• Develop CI/CD pipelines to automate model and application deployment across environments.
• Build and manage data and ML workflows using tools such as Python, Airflow/Composer, Git, and cloud platforms.
• Deploy and manage ML models using platforms such as Vertex AI or equivalent ML platforms.
• Implement model monitoring, performance tracking, logging, alerting, and retraining workflows.
• Work closely with Data Scientists, Data Engineers, and Software Engineers to productionize ML models.
• Develop APIs and services to integrate ML models with downstream applications.
• Ensure solutions are scalable, reliable, secure, and maintainable.
• Troubleshoot production issues and optimize ML pipelines for performance and reliability.
• Follow software engineering best practices including code reviews, testing, documentation, and version control.