Senior Machine Learning Engineer

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

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

Some of our benefits

• SoftServe University, with learning opportunities, certification support and leadership opportunities to help you grow your skills
• Medical insurance for you and your family
• Food vouchers
• Extra days off
• Dare to Care, our holistic well-being program
• Anniversary Program to celebrate your time with us