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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Engineer - Remote Role - **Company:** TRENT HOEK OUTDOORS LLC - **Location:** United States (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Agile Methodology, Artificial Intelligence, Airflow, Amazon Web Services, Software Applications, Automation of Tests, Microsoft Azure, Batch Processing, Business Software, Cloud Computing, Software Quality, Code Review, Continuous Integration, Data Cleansing, Information Engineering, Software Debugging, Distributed Systems, Monitoring of Systems, Python (Programming Language), Machine Learning, Natural Language Processing, Recommender Systems, Tensorflow, Azure Machine Learning, Software Deployment, Software Engineering, SQL Databases, Data Processing, Google Cloud, Enterprise Software Applications, Cloud Platform System, Feature Engineering, Pytorch, Retrieval-Augmented Generation, Large Language Models, Apache Spark, Google Vertex AI, IT Architecture, Deep Learning, Model Validation, Reliability of Systems, Generative AI, AI Platforms, Scikit Learn, Kubernetes, Real-time Inference, Information Technology, Optimization Algorithms, HuggingFace, Xgboost, Machine Learning Operations, Restful APIs, Software Version Control, Data Pipelines, Docker, Unsupervised Learning, Databricks, Microservices - **Published:** October 16, 2026 - **Apply:** https://arc.dev/remote-jobs/j/redirect/ps08629cjg ## About the Role We are seeking an experienced Machine Learning Engineer with at least 5 years of professional experience to design, build, deploy, and optimize machine learning systems and AI-powered applications in an enterprise technology environment., * At least 5 years of professional experience in Machine Learning Engineering, Applied Machine Learning, AI Engineering, or a closely related software engineering field. * Bachelor's degree in Computer Science, Machine Learning, Artificial Intelligence, Data Science, Mathematics, Engineering, or a related technical discipline, or equivalent practical experience. * Advanced proficiency in Python and a strong understanding of software engineering principles. * Hands-on experience with PyTorch, TensorFlow, scikit-learn, XGBoost, or comparable machine learning frameworks. * Experience developing, evaluating, and deploying models in production environments. * Strong knowledge of supervised and unsupervised learning, feature engineering, model evaluation, and optimization techniques. * Experience with MLOps tools and practices, including MLflow, Kubeflow, model registries, and experiment tracking, or equivalent solutions. * Familiarity with AWS, Microsoft Azure, or Google Cloud Platform (GCP). * Experience with Docker, Kubernetes, REST APIs, CI/CD pipelines, and scalable deployment practices. * Strong understanding of SQL, data processing, and distributed computing fundamentals. * Familiarity with Apache Spark, Databricks, Airflow, or related data processing and orchestration technologies. * Understanding of model monitoring, observability, data drift, and production reliability. * Strong analytical, debugging, problem-solving, and technical communication skills., * Experience with Generative AI, large language models, NLP, Hugging Face, vector databases, or RAG architectures. * Familiarity with cloud-based machine learning services such as AWS SageMaker, Azure Machine Learning, or Google Vertex AI. * Experience contributing to reusable ML infrastructure, deployment frameworks, or enterprise AI platforms. * Knowledge of model governance, reproducibility, and responsible AI practices. What You'll Bring * Strong analytical reasoning and the ability to solve complex technical problems. * The ability to translate machine learning concepts into practical, maintainable engineering solutions. * A focus on code quality, model performance, and system reliability. * Ownership of machine learning solutions throughout the development lifecycle. * The ability to balance accuracy, computational efficiency, scalability, and business impact. * Effective collaboration with engineering, research, product, and data teams. * Clear communication with technical and nontechnical stakeholders. * The ability to work independently in a distributed or remote engineering environment. A commitment to responsible AI development and continuous learning. ## Description This role is suited to an engineer with strong Python programming skills, hands-on machine learning and deep learning experience, and a solid understanding of MLOps, cloud infrastructure, and production deployment. You will work with Data Scientists, AI Researchers, Software Engineers, Data Engineers, Cloud Infrastructure teams, and Product Managers to turn machine learning models into reliable, scalable applications., * Design, train, evaluate, and deploy machine learning models for enterprise applications. * Build and maintain ML pipelines for data preprocessing, feature engineering, model training, evaluation, and inference. * Develop production-ready solutions using Python, PyTorch, TensorFlow, scikit-learn, XGBoost, and related frameworks. * Implement and optimize deep learning models for classification, forecasting, recommendation systems, and natural language processing. * Deploy models through REST APIs, microservices, batch-processing workflows, and real-time inference services. * Build and maintain MLOps workflows for model versioning, experiment tracking, automated testing, CI/CD, and model monitoring. * Work with Data Engineering teams to maintain dependable data pipelines and ensure feature availability. * Improve model accuracy, inference latency, scalability, and cloud resource efficiency. * Monitor deployed models for data drift, performance degradation, and operational issues. * Identify opportunities to apply Generative AI, large language models (LLMs), retrieval-augmented generation (RAG), and AI automation to business applications. * Apply responsible AI practices covering explainability, privacy, security, and reliability. * Participate in architecture and code reviews, technical documentation, and engineering design discussions. * Investigate and resolve complex machine learning infrastructure and production issues. * Work with cross-functional stakeholders to translate business requirements into practical ML solutions., * Contribute to enterprise AI strategy and machine learning platform modernization. * Help develop scalable, reusable ML infrastructure and deployment frameworks. * Support the adoption of intelligent automation and AI-driven business applications. * Evaluate emerging technologies in Generative AI, LLMs, deep learning, and MLOps. * Improve model governance, reproducibility, monitoring, and operational efficiency. * Collaborate with technical leadership on AI architecture and long-term engineering roadmaps. * Identify opportunities to improve business outcomes through production machine learning solutions., * Work Environment: Enterprise AI and software engineering teams collaborating across multiple U.S. time zones. * Working Conditions: Participation in Agile development, technical reviews, model releases, and engineering planning. Occasional production support or scheduled deployment work may be required. * Data Responsibility: Responsible handling of confidential enterprise information, proprietary source code, and sensitive datasets is essential.