Generative AI & ML Scientist: NLP, RAG & Agents

SoftServe; Poland; On-site; PLN 180,000 - 260,000; Full time

Job summary

SoftServe is seeking a data scientist/ML engineer to develop Generative AI and ML solutions as part of our AI and Data Science Center of Excellence, a team of 170+ experts. You will work on NLP pipelines, RAG applications, and agentic workflows alongside experienced researchers and engineers to deliver measurable client impact.

Responsibilities include building ML pipelines, deploying models in cloud environments (AWS, Azure, GCP), and collaborating with cross-functional teams.

Qualifications

• At least 3 years of experience in data science or ML with hands-on model development.
• Master's degree in Computer Science or a related field.
• Proficiency in Python and frameworks like PyTorch or TensorFlow.
• Experience with Generative AI, NLP techniques, transformer models and LLMs.
• Familiarity with LangGraph or CrewAI for multi-agent applications.
• Experience with cloud AI/ML services (AWS, Azure, GCP) and ML lifecycle.

Responsibilities

• Develop and apply Generative AI and ML solutions across NLP, RAG, and agentic workflows.
• Build and maintain ML pipelines from data analysis to deployment.
• Collaborate with data scientists, engineers, and stakeholders across client projects.
• Contribute to agentic workflows using LangGraph or CrewAI.
• Document findings and translate insights for technical and non-technical audiences.

Skills

Python

ML pipelines

Data analysis

English proficiency

Communication

Education

Master's degree in Computer Science

Tools

PyTorch

TensorFlow

LangGraph

CrewAI

AWS

Azure

GCP

Big Data tools

Job description

SoftServe is seeking a data scientist/ML engineer to develop Generative AI and ML solutions as part of our AI and Data Science Center of Excellence, a team of 170+ experts. You will work on NLP pipelines, RAG applications, and agentic workflows alongside experienced researchers and engineers to deliver measurable client impact.

Responsibilities include building ML pipelines, deploying models in cloud environments (AWS, Azure, GCP), and collaborating with cross-functional teams.