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# ML Engineer

- **Company:** WINNING EDGE
- **Location:** Hartford, CT, United States
- **Contract:** Temporary contract
- **Skills:** Automation of Tests, Information Engineering, Monitoring of Systems, Python (Programming Language), Machine Learning, Performance Tuning, Azure Machine Learning, Management of Software Versions, Deployment Automation, Machine Learning Operations
- **Published:** August 5, 2026
- **Apply:** https://www.dice.com/job-detail/a830dcd9-9b38-47ff-ac9f-2de5321c246c

## About the Role

Key Skills: Machine Learning, Python, ML Pipelines, Model Deployment, Model Serving, Data Engineering, Model Monitoring, Drift Detection, Performance Optimization, Automated Testing

## Description

Role Summary: Builds, trains and tunes machine learning models. Translates data science experiments into scalable, production-ready ML solutions.

Responsibilities:

Translate data science prototypes into production-grade ML services and pipelines.

Build training and inference code with reproducibility, versioning, and automated testing.

Implement scalable model serving (online/offline), batching, and latency/throughput optimization.

Integrate model lifecycle tooling (tracking, registry, deployment automation, monitoring).

Collaborate with Data Engineering on feature pipelines and data contracts.

Own production health: drift detection, performance regression, rollback strategies, and incident response.


## Related Videos

- [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning)
- [DevOps for Machine Learning](https://www.wearedevelopers.com/videos/179-devops-for-machine-learning)
- [Practical performance tuning for Serverless Java on AWS](https://www.wearedevelopers.com/videos/2075-practical-performance-tuning-for-serverless-java-on-aws)
- [How to add test automation to your project: The good, the bad, and the ugly](https://www.wearedevelopers.com/videos/1668-how-to-add-test-automation-to-your-project-the-good-the-bad-and-the-ugly)
- [What non-automotive Machine Learning projects can learn from automotive Machine Learning projects](https://www.wearedevelopers.com/videos/397-what-non-automotive-machine-learning-projects-can-learn-from-automotive-machine-learning-projects)
- [Tour de Force: Open-Source LLM Inference Optimization from Simple to Sophisticated](https://www.wearedevelopers.com/videos/100099-tour-de-force-open-source-llm-inference-optimization-from-simple-to-sophisticated)

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- [Dev Digest 121 - AI goes offline](https://www.wearedevelopers.com/magazine/456-dev-digest-121-ai-goes-offline)
- [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models)