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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Engineer - **Company:** PWN LLC - **Location:** United States (Remote available) - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Amazon Web Services, Computer Vision, Automation of Tests, Microsoft Azure, Code Review, Cyber Security, Databases, Data Validation, Data Cleansing, Distributed Data Store, Monitoring of Systems, Python (Programming Language), Machine Learning, Natural Language Processing, Recommender Systems, Tensorflow, Standard Sql, Systems Integration, Strategies of Testing, Unstructured Data, Software Organization, Google Cloud, Cloud Platform System, Feature Engineering, Pytorch, Retrieval-Augmented Generation, Large Language Models, Apache Spark, Model Validation, Backend, Git, AI Platforms, Scikit Learn, Kubernetes, Production Code, Xgboost, Machine Learning Operations, Restful APIs, Software Version Control, Docker, Unsupervised Learning - **Published:** July 22, 2026 - **Apply:** https://arc.dev/remote-jobs/j/redirect/p6l9w15vlx ## About the Role Professional working proficiency in both English and Russian is mandatory due to regular communication with English- and Russian-speaking team members and stakeholders., * At least 3 years of commercial experience as a Machine Learning Engineer, AI Engineer, Data Scientist, or in a comparable technical role. * Strong proficiency in Python. * Production experience with one or more machine learning frameworks, including PyTorch, TensorFlow, Scikit-learn, XGBoost, or LightGBM. * Strong understanding of supervised and unsupervised learning. * Practical knowledge of classification, regression, clustering, ranking, forecasting, or recommendation systems. * Experience with data preprocessing, feature engineering, model evaluation, and hyperparameter optimization. * Strong knowledge of SQL and experience working with relational or analytical databases. * Experience deploying machine learning models into production. * Experience designing or integrating REST APIs. * Working knowledge of Git, automated testing, code review, and software development best practices. * Understanding of statistics, probability, linear algebra, and machine learning fundamentals. * Ability to write maintainable, tested, and well-documented production code. * Strong analytical, communication, and problem-solving skills. * Ability to work independently and effectively within a distributed, cross-functional team. * Professional working proficiency in English, both written and spoken. * Professional working proficiency in Russian, both written and spoken. Preferred Qualifications * Experience with AWS, Microsoft Azure, or Google Cloud Platform. * Experience with Docker and containerized model deployment. * Knowledge of Kubernetes and cloud-native infrastructure. * Experience with MLOps tools such as MLflow, Kubeflow, Airflow, Weights & Biases, or similar platforms. * Experience building CI/CD pipelines for machine learning systems. * Knowledge of model monitoring, observability, data validation, and drift detection. * Experience with distributed data-processing technologies such as Spark. * Experience with natural language processing, large language models, computer vision, recommendation systems, or predictive analytics. * Familiarity with retrieval-augmented generation, vector databases, embeddings, or LLM evaluation. * Understanding of data privacy, information security, responsible AI, and model governance. * Experience working in an international or multilingual environment., We evaluate candidates based on professional qualifications, relevant experience, technical ability, and alignment with the requirements of the role. We are committed to maintaining a professional and inclusive recruitment process. ## Description We are seeking an experienced Machine Learning Engineer to design, develop, deploy, and maintain production-grade machine learning solutions. You will be responsible for the complete machine learning lifecycle, from data preparation and experimentation to production deployment, monitoring, and continuous improvement. The role requires close collaboration with software engineers, product managers, data specialists, and international stakeholders., * Design, develop, train, validate, and optimize machine learning models. * Build scalable and reliable machine learning pipelines. * Prepare, clean, transform, and analyze structured and unstructured datasets. * Perform feature engineering, model selection, hyperparameter tuning, and error analysis. * Define appropriate model evaluation metrics and validation strategies. * Deploy machine learning models and AI services into production environments. * Develop and maintain APIs and backend services for model inference. * Monitor model performance, latency, data quality, and model drift. * Improve the reliability, scalability, and cost efficiency of machine learning systems. * Establish reproducible experimentation, model versioning, and documentation practices. * Collaborate with engineering and product teams to translate business requirements into technical solutions. * Conduct code reviews and contribute to engineering standards and best practices. * Research and evaluate new machine learning methods, frameworks, and technologies. * Clearly communicate technical findings and recommendations to both technical and non-technical stakeholders. ## Related Videos - [Multilingual NLP pipeline up and running from scratch](https://www.wearedevelopers.com/videos/901-multilingual-nlp-pipeline-up-and-running-from-scratch) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [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) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) - [Docker build without Docker](https://www.wearedevelopers.com/videos/100114-docker-build-without-docker) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [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) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Fully Remote Software Engineer Jobs](https://www.wearedevelopers.com/magazine/447-fully-remote-software-engineer-jobs) - [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)