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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Engineer - **Company:** Focus LLC - **Location:** Boston, MA, United States - **Experience:** Experienced - **Salary:** $160,000.0 - $180,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Automation of Tests, Microsoft Azure, Big Data, Cloud Computing, Continuous Integration, Distributed Computing Environment, Python (Programming Language), Machine Learning, Performance Tuning, Tensorflow, Azure Machine Learning, Software Construction, Software Deployment, Software Engineering, Data Logging, Google Cloud, Feature Engineering, Data Ingestion, Pytorch, Snowflake, Apache Spark, Model Validation, Git, Pandas, Scikit Learn, Infrastructure Automation Frameworks, Information Technology, Dask, Machine Learning Operations, Software Version Control, Data Pipelines, Docker, Databricks - **Published:** August 22, 2026 - **Apply:** https://www.dice.com/job-detail/e9a329ec-a19e-4a05-a37b-16260afcc2dc ## About the Role * Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related technical field. * 3+ years of experience in machine learning engineering, applied ML, or related software engineering roles. * Strong proficiency in Python and experience with modern ML frameworks such as TensorFlow, PyTorch, or scikit-learn. * Experience with distributed data processing and compute frameworks (e.g., Pandas, Spark, Dask). * Hands-on experience with containerization and orchestration technologies such as Docker and Kubernetes. * Familiarity with CI/CD pipelines, testing automation, and version control using Git. * Experience working with cloud-based ML platforms or services (e.g., SageMaker, Vertex AI, Databricks, or Snowflake ML) is preferred. * Strong understanding of model evaluation, feature engineering, and performance optimization in production contexts. * Excellent analytical, communication, and collaboration skills, with the ability to work effectively in cross-functional teams. ## Description We are seeking a skilled Machine Learning Engineer with approximately three years of hands-on experience designing, deploying, and maintaining production-grade machine learning systems. In this role, you will collaborate closely with data scientists, software engineers, and product teams to translate research models into reliable, scalable, and high-impact applications. You will be deeply involved in the end-to-end ML lifecycle-from data ingestion and feature engineering to deployment, monitoring, and continuous improvement-playing a critical part in shaping our machine learning platform and capabilities., * Develop, deploy, and optimize machine learning models for real-world business use cases and client-facing applications. * Partner with data scientists to operationalize predictive models and ensure scalable, maintainable, and performant production deployments. * Design and implement data pipelines and workflows that support training, inference, and model lifecycle management. * Work with large, complex datasets to ensure data quality, reproducibility, and reliable version control across ML workflows. * Implement model monitoring, logging, and alerting strategies to track performance, detect drift, and support retraining cycles. * Leverage cloud platforms (AWS, Azure, Google Cloud Platform) to build scalable ML solutions using managed services and infrastructure-as-code practices. * Write clean, modular, and well-documented code aligned with MLOps and software engineering best practices. * Stay current on emerging ML tooling, frameworks, and industry best practices to continuously enhance our platform and capabilities. ## Related Videos - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [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) - [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning) - [Docker build without Docker](https://www.wearedevelopers.com/videos/100114-docker-build-without-docker) - [Git for Code Reviews](https://www.wearedevelopers.com/videos/429-git-for-code-reviews) - [Effective Machine Learning - Managing Complexity with MLOps](https://www.wearedevelopers.com/videos/185-effective-machine-learning-managing-complexity-with-mlops) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [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) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Got AI ideas but no money? Here are 10 free ways to level up your AI skills with Google Cloud](https://www.wearedevelopers.com/magazine/600-got-ai-ideas-but-no-money-here-are-10-free-ways-to-level-up-your-ai-skills-with-google-cloud) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development)