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# Middle ML Engineer

- **Company:** SoftServe, Inc.
- **Location:** United States (Remote available)
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Airflow, Amazon Web Services, Microsoft Azure, Cloud Computing, Cloud Engineering, Continuous Integration, Data Stores, Python (Programming Language), Machine Learning, Azure Machine Learning, Management of Software Versions, Cloud Platform System, Feature Engineering, Data Ingestion, Multi-Agent Systems, Kubernetes, Information Technology, Machine Learning Operations, Virtual Agents, Data Pipelines, Databricks
- **Published:** August 25, 2026
- **Apply:** https://arc.dev/remote-jobs/j/redirect/pf1g7d5r0s

## About the Role

* 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

## Description

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., * 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


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