Middle Data Scientist (Generative AI)
Indexed description
SoftServe
About The Role
In this role, you will develop and apply Generative AI and machine learning solutions — from NLP pipelines and RAG applications to agentic workflows — as part of SoftServe's AI and Data Science Center of Excellence, a team of 170+ experts, including Data Scientists, ML Engineers, and Architects. Working alongside experienced researchers and engineers, you'll contribute to data-driven projects that deliver measurable impact for clients.
Responsibilities
- Implement and contribute to Generative AI and agentic solutions — including RAG pipelines, intelligent assistants, and NLP applications — working alongside senior team members
- Build and maintain ML pipelines covering data analysis, experiment design, model training, and deployment, supporting reliability and reproducibility in production
- Collaborate with Data Scientists, Engineers, and stakeholders to understand requirements and contribute to production-ready ML solutions across diverse client projects
- Contribute to agentic workflows using frameworks such as LangGraph or CrewAI, supporting the development of multi-agent systems and autonomous AI applications
- Apply deep learning techniques in Python with PyTorch or TensorFlow to experiment, prototype, and iterate on solutions across NLP and generative AI domains
- Work with Big Data tools and cloud platforms (AWS, GCP, or Azure) to support data-driven experimentation, feature engineering, and analysis tasks
- Document and communicate experimental findings, translating data-driven insights into clear, actionable conclusions for both technical and non-technical audiences
- At least 3 years of experience in data science or machine learning, with hands-on exposure to building and experimenting with ML models
- Master's degree in Computer Science or a related field
- Solid Python proficiency with experience in PyTorch or TensorFlow for model development and experimentation
- Foundational knowledge of Generative AI and NLP techniques, including transformer models and LLMs such as GPT or Claude
- Familiarity with agentic AI concepts and frameworks, such as LangGraph or CrewAI, for building multi-agent applications
- Exposure to cloud-based AI/ML services on AWS, Azure, or GCP, and understanding of the ML development lifecycle
- Familiarity with Big Data tools and data processing in cloud environments
- Strong analytical and communication skills
- Upper-intermediate or higher proficiency in English, both spoken and written
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