Senior Data Scientist

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This is a permanent role with Series A funded startup in IT domain, a valued client of HyrEzy.

About The Role

Key Responsibilities

• Design, develop, and implement advanced machine learning models with a focus on generative and agentic AI architectures.
• Work extensively with Large Language Models (LLMs)—including fine-tuning, evaluation, and integration into business applications.
• Deploy, monitor, and automate ML workflows using modern MLOps frameworks (such as MLflow, Kubeflow, or similar).
• Collaborate with cross-functional teams to gather requirements, define problem statements, and translate business needs into technical solutions.
• Perform data preprocessing, feature engineering, and exploratory data analysis (EDA) on diverse, large-scale datasets.
• Develop robust pipelines for continuous integration and deployment of AI/ML models in production.
• Stay current with industry advancements in AI/ML, particularly generative AI and foundation models, recommending adoption of best practices as appropriate.
• Communicate findings, progress, and outcomes clearly with both technical and non-technical stakeholders.
  

Required Skills And Qualifications

• Bachelor’s/Master’s degree in Computer Science, Data Science, Engineering, Statistics, or a related field.
• 3+ years of professional experience in data science and machine learning, including at least one year working with generative or large language models.
• Proficiency in Python and experience with AI/ML libraries such as TensorFlow, PyTorch, Hugging Face Transformers, or similar.
• Hands-on experience implementing MLOps solutions throughout the ML lifecycle (model tracking, deployment, monitoring, retraining).
• Strong understanding of generative models, reinforcement learning, and/or agentic AI systems.
• Proven ability to analyze complex datasets, visualize results, and present actionable insights.
• Excellent problem-solving, communication, and collaboration skills.
  

Preferred Experience

• Experience with prompt engineering, LLM APIs, or building applications leveraging foundation models.
• Publication or open-source contributions in AI/ML domains.
• Prior work in cloud environments (AWS, Azure, GCP) for scalable model deployment.