> Markdown version of [/jobs/ext/2573051-machine-learning-operations-engineer](https://www.wearedevelopers.com/jobs/ext/2573051-machine-learning-operations-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Machine Learning Operations Engineer

- **Company:** Ipolarity LLC
- **Location:** Hanover, NJ, United States
- **Experience:** Experienced
- **Salary:** $114,400.0 - $124,800.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Cloud Computing, Data Validation, Information Engineering, Monitoring of Systems, Python (Programming Language), Machine Learning, Azure Machine Learning, Software Engineering, Management of Software Versions, AI Infrastructure, Sql Optimization, Delivery Pipeline, Apache Spark, Git, Cloudformation, Pyspark, Kubernetes, Information Technology, Machine Learning Operations, Terraform, Databricks
- **Published:** August 2, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=d5882bdc5a806f54

## About the Role

* 8+ years - 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.

Education: Bachelor's or Master's degree in Computer Science, Software Engineering, Data Engineering, or a related technical field.
Related Work Experience: 3+ years supporting production machine learning platforms or cloud infrastructure.
Technical Skills:

 * Advanced SQL
 * Python
 * Apache Spark / PySpark
 * AWS SageMaker (Databricks, Azure ML, or Vertex AI experience is a plus)
 * 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

## Description

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.
What You'll Do:

 * 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.


## Related Videos

- [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data)
- [Infrastructure as Code: The Developer's Secret Weapon](https://www.wearedevelopers.com/videos/1221-infrastructure-as-code-the-developer-s-secret-weapon)
- [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94)
- [Effective Machine Learning - Managing Complexity with MLOps](https://www.wearedevelopers.com/videos/185-effective-machine-learning-managing-complexity-with-mlops)
- [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail)
- [Empowering Retail Through Applied Machine Learning](https://www.wearedevelopers.com/videos/976-empowering-retail-through-applied-machine-learning)

## Related Articles

- [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it)
- [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development)
- [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production)
- [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer)
- [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production)
- [Dev Digest 162: AI careers, MCP, AWS best practices & floppy sweaters](https://www.wearedevelopers.com/magazine/571-dev-digest-162-ai-careers-mcp-aws-best-practices-floppy-sweaters)