Middle Machine Learning Engineer

In this role you will build and maintain end-to-end ML systems — from data pipelines and model training to LLMOps tooling and agentic workflows — as part of SoftServe’s AI and Data Science Center of Excellence. Working alongside 170 experienced ML engineers, data scientists, and architects, you’ll contribute to cutting-edge NLP, RAG, and multimodal AI projects that deliver real impact for clients.

Responsibilities

• Implement and maintain end-to-end ML pipelines supporting data ingestion, feature engineering, model training, and deployment into production environments
• Build and support LLMOps pipelines using tools such as MLflow, Langfuse, or LangSmith, contributing to model observability, reproducibility, and prompt versioning across projects
• Collaborate with Data Scientists, Senior Engineers, and stakeholders to understand requirements and contribute to production-ready ML solutions for NLP, RAG systems, and multimodal models
• Contribute to the development of agentic systems and multi-agent workflows using frameworks such as LangGraph or CrewAI, supporting autonomous AI applications
• Support and improve ML infrastructure tasks, including CI/CD pipelines, cloud environments on AWS, Azure, or GCP, data stores, and monitoring tooling
• Integrate and package ML services into real applications, writing clean, maintainable code that meets engineering and quality standards
• Configure and maintain workflow orchestration pipelines using tools such as Kubeflow, Airflow, or Databricks Workflows

• At least 2 relevant years of hands-on experience building and deploying ML solutions, with exposure to Cloud production environments
• Solid Python proficiency across the data science and ML ecosystem, including model development and basic service integration
• Familiarity with LLMOps and experiment tracking tools such as MLflow, Langfuse, or LangSmith
• Understanding of CI/CD practices for ML systems and workflow orchestration tools such as Kubeflow, Airflow, or Databricks Workflows
• Experience with cloud-based AI/ML services on AWS, Azure, or GCP
• Basic knowledge of agentic AI concepts and frameworks, such as LangGraph or CrewAI
• Master’s degree in Computer Science or a related field
• Upper-intermediate or higher proficiency in English, both spoken and written