Summary
✨ AI‑Generated
A senior machine learning engineering role focused on taking advanced AI systems from data ingestion and experimentation through optimization, observability, and production deployment. You will build LLMOps infrastructure, develop NLP and retrieval-based solutions, work with multimodal models and agentic workflows, and collaborate closely with technical and business stakeholders.
Highlights
Work on end-to-end machine learning systems at production scale, combining applied research with real-world delivery. Collaborate with experienced AI/ML specialists, data scientists, engineers, and clients on advanced NLP, retrieval, and multimodal AI solutions.
Description
About The Role
In this role you will engineer and productionize end-to-end ML systems — from data pipelines and LLMOps infrastructure to agentic multi-agent workflows — as part of SoftServe's AI and Data Science Center of Excellence, a community of over 170 AI/ML experts.
You'll work at the intersection of applied research and real-world delivery, collaborating with data scientists, engineers, and clients to bring cutting-edge NLP, RAG, and multimodal AI solutions to production scale.
Responsibilities
Design and implement end-to-end ML pipelines — from data ingestion and feature engineering to model training, optimization, and production deploymentBuild and maintain LLMOps pipelines using MLflow, Langfuse, LangSmith, or Weights & Biases to enable model observability, reproducibility, and prompt versioningCollaborate with Data Scientists, Engineers, and clients to translate business requirements into production-ready ML solutions for NLP, RAG systems, and multimodal modelsDevelop and orchestrate agentic systems and multi-agent workflows using frameworks such as LangGraph or CrewAI, supporting autonomous AI applications at scaleEnhance and manage ML infrastructure including CI/CD/CT pipelines, cloud environments on AWS, Azure, or GCP, data stores, monitoring, and securityIntegrate and package ML services into real applications, ensuring they meet reliability and maintainability standards for production useOperate workflow orchestration tools such as Databricks Jobs/Workflows, Kubeflow, or Airflow to automate and monitor ML pipeline execution
Requirements
Minimum 3 years of hands-on experience building and deploying real-world ML solutions in productionMaster's degree in Computer Science or a related fieldStrong Python proficiency across the core data science and ML ecosystem, including model development, packaging, and service integrationAdvanced experience with LLMOps, AgentOps, and experiment tracking tools such as MLflow, Langfuse, LangSmith, and Weights & BiasesSolid knowledge of CI/CD/CT practices for ML systems and workflow orchestration tools such as Databricks Workflows, Kubeflow, or AirflowProven experience with cloud-based AI/ML services on AWS, Azure, or GCPWorking knowledge of agentic AI frameworks, including LangGraph, CrewAI, or similar tools for building autonomous and multi-agent systemsUpper-intermediate or higher proficiency in English, both spoken and written
SoftServe is an equal opportunity employer.
Qualified applicants will receive consideration regardless of race, color, ancestry, ethnicity, national origin, religion, sex, sexual orientation, gender identity or expression, age, citizenship, disability, health condition, marital or family status, veteran status, or any other characteristic protected by applicable law.
