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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Engineer - **Company:** Ascent, LLC - **Location:** Ann Arbor, MI, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Airflow, Big Data, Computer Programming, Continuous Integration, Data Transformation, Distributed Computing Environment, Monitoring of Systems, Statistical Hypothesis Testing, Python (Programming Language), Machine Learning, Tensorflow, Azure Machine Learning, Unstructured Data, Cloud Platform System, Data Ingestion, Pytorch, Deep Learning, Model Validation, Containerization, Pyspark, Scikit Learn, Kubernetes, Information Technology, Xgboost, Machine Learning Operations, Software Version Control, Docker - **Published:** July 17, 2026 - **Apply:** https://www.dice.com/job-detail/11ad1255-cdf4-491b-b65a-e46c6929dca5 ## About the Role We are looking for an experienced Senior Machine Learning Engineer with deep expertise in statistical and machine learning techniques, large-scale data processing, and model deployment in cloud environments. The ideal candidate will be a self-starter with strong problem-solving skills and hands-on experience in building and deploying ML models using big data technologies like PySpark and cloud platforms like Amazon SageMaker., * 7+ years of experience in machine learning, data science, or related fields. * Strong programming skills in Python with experience in ML libraries (e.g., scikit-learn, XGBoost, TensorFlow, PyTorch). * Hands-on experience with PySpark for big data processing and model development. * Proficient in building models on large-scale datasets (terabytes to petabytes). * Solid understanding of statistical analysis, probability, hypothesis testing, and experimental design. * Experience with Amazon SageMaker (or similar cloud-based ML platforms). * Strong knowledge of ML Ops practices including version control, model monitoring, and retraining strategies. * Familiarity with containerization (Docker) and CI/CD practices for ML projects is a plus. * Excellent communication skills and the ability to clearly explain complex concepts to non-technical stakeholders., * Master's or Ph.D. in Computer Science, Statistics, Mathematics, or a related quantitative discipline. * Experience with workflow orchestration tools (e.g., Airflow, Kubeflow). * Prior experience in domains like Manufacturing, finance, healthcare, or e-commerce is a plus. ## Description * Design, develop, and deploy scalable machine learning models for real-world business problems using structured and unstructured data. * Analyze large datasets using PySpark and other distributed computing frameworks to extract insights and prepare features for ML pipelines. * Apply a wide range of statistical, machine learning, and deep learning techniques, including but not limited to regression, classification, clustering, time-series forecasting, and NLP. * Own end-to-end ML pipelines from data ingestion, preprocessing, training, validation, tuning, and deployment. * Utilize Amazon SageMaker or similar platforms for building, training, and deploying models in a production-grade environment. * Collaborate closely with data engineers, data scientists, and product teams to integrate models with business workflows. * Monitor and improve model performance, scalability, and reliability in production. * Contribute to setting up and maintaining the ML environment and tooling (including environment configuration, CI/CD pipelines for ML, model versioning, etc.). ## Related Videos - [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data) - [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning) - [TikTok's Privacy Innovation](https://www.wearedevelopers.com/videos/1036-tiktok-s-privacy-innovation) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [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 - [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) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development)