Senior Machine Learning Engineer
# Senior Machine Learning Engineer **Softserve** · Remote · `Remote` 🕒 **Статус:** *Опубликовано: сегодня* · *Источник: Get on Board (LATAM)* --- ### About the Role - Strong experience with building and deploying production-grade machine learning solutions, supported by at least three years of hands-on industry experience - Advanced knowledge of Python across the machine learning and data science ecosystem, including model development, packaging, and service integration - Hands-on expertise with LLMOps, AgentOps, and experiment tracking tools such as MLflow, Langfuse, LangSmith, and Weights & Biases - Solid understanding of CI/CD/CT practices for machine learning systems together with workflow orchestration platforms including Databricks Workflows, Kubeflow, or Airflow - Proven experience with cloud-based AI and machine learning services on AWS, Azure, or Google Cloud Platform - Working knowledge of agentic AI frameworks such as LangGraph, CrewAI, or similar technologies for building autonomous and multi-agent systems - Master’s degree in Computer Science or a related field - Strong communication skills with Upper-Intermediate or higher English proficiency, enabling effective collaboration with cross-functional teams and clients - Design end-to-end machine learning pipelines covering data ingestion, feature engineering, model training, optimization, and production deployment to support reliable AI solutions - Build and maintain LLMOps pipelines using experiment tracking and observability tools to improve model reproducibility, prompt management, and production visibility across ML systems - Collaborate with data scientists, engineers, and clients to transform business requirements into production-ready solutions for NLP, retrieval-augmented generation, and multimodal AI applications - Develop agentic systems and multi-agent workflows using frameworks such as LangGraph, CrewAI, or similar technologies to support autonomous AI capabilities at production scale - Enhance machine learning infrastructure by maintaining CI/CD/CT pipelines, cloud environments, data stores, monitoring processes, and security practices that support dependable operations - Integrate and package machine learning services into production applications while ensuring reliability, maintainability, and long-term operational stability - Operate workflow orchestration platforms such as Databricks Jobs and Workflows, Kubeflow, or Airflow to automate, schedule, and monitor machine learning pipeline execution
Наблюдалась 2026-10-16, впервые 2026-10-15, источник — Get on Board (LATAM).