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Medium: mid-level DS title and 5+ years attract applicants, but niche agentic AI skills narrow pool.
Medium — core ML skills transfer easily, but agentic AI and R&D domain specifics favor similar industries.
High due to mandatory 5+ years, advanced degree, and required agentic AI, MLOps, and deployment experience.
Design, build, and deploy autonomous AI agents leveraging large language models and orchestration frameworks to solve complex business and R&D problems.
Lead end-to-end machine learning projects including data acquisition, feature engineering, model development, and deployment in production environments with scalability and reliability.
Collaborate with clients to define project objectives and deliver actionable analytics-driven insights using advanced statistical and machine learning techniques, including computer vision applications.
Master’s or Ph.D. in Data Science, Computer Science, Statistics, or related quantitative field.
5+ years of experience building and deploying machine learning models, with hands-on experience in Agentic AI systems.
Proficiency in Python/R and deep learning frameworks such as PyTorch, TensorFlow, or Keras; experience with MLOps, Git, and cloud platforms (AWS/GCP/Azure).
Work Experience Required: 5+ years; Notice Period: Not explicitly mentioned in the JD.
Experienced in designing and deploying intelligent, autonomous AI systems focused on agentic architectures and advanced ML workflows.
Capable of independently managing multiple machine learning projects from conception through deployment with strong technical leadership.
Skilled at partnering with business stakeholders to translate complex analytical problems into practical solutions, especially in R&D and product innovation contexts.