Senior Machine Learning Engineer

About the Role

In this role, you will help turn advanced machine learning ideas into reliable production solutions that deliver real business value. You will take ownership of building and scaling end-to-end ML systems while working within SoftServe’s AI and Data Science Center of Excellence, a community of more than 170 AI and ML specialists. Your work will connect applied research with production delivery through close collaboration with engineers, data scientists, and clients to create practical AI solutions with measurable impact

Responsibilities

• 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

Requirements

• 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