Machine Learning Operations Engineer

BDIPlus•New York, NY

2d•$110,000 - $130,000

About The Position

We are seeking a Machine Learning Operations (MLOps) Engineer to join our team. The MLOps Engineer will be responsible for building and maintaining the infrastructure that enables reliable deployment, monitoring, governance, and continuous improvement of production machine learning systems across enterprise client environments.

Requirements

Passionate about building reliable AI infrastructure at enterprise scale.
Experienced deploying and maintaining production machine learning systems.
Strong analytical and troubleshooting skills.
Fast learner with attention to detail.
Excellent communication and collaboration skills.
Comfortable working with both software engineering and data science teams.
Advanced SQL
Python
Apache Spark / PySpark
AWS SageMaker
Kubernetes
CI/CD pipelines
Infrastructure as Code (Terraform, CloudFormation, or similar)
Model monitoring and ML observability tools
Data validation and automated testing frameworks
Statistics related to monitoring, model drift, and performance evaluation
Git and modern DevOps practices

Nice To Haves

Databricks, Azure ML, or Vertex AI experience

Responsibilities

Design, build, and maintain scalable machine learning deployment pipelines.
Develop standardized model registries, artifact repositories, data versioning, and reproducible ML environments.
Build automated evaluation pipelines for production machine learning models.
Implement automated data quality monitoring including profiling, anomaly detection, validation, quarantine, and alerting.
Develop automated retraining workflows, promotion gates, rollback capabilities, and audit trails.
Monitor production environments for model drift, latency, prediction quality, infrastructure performance, and operational costs.
Troubleshoot production machine learning issues and lead incident response activities.
Build CI/CD pipelines supporting enterprise AI applications.
Collaborate closely with Data Scientists and client engineering teams to deploy and maintain AI solutions.
Ensure governance, security, lineage, reproducibility, and audit readiness across machine learning platforms.