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Tier-1 employer, metro location, and mid-level data scientist title increase candidate competition.
GenAI specialization with embedded/edge integration and regulatory healthcare context reduces cross-industry transferability.
Explicit 5/7 years requirement plus mandatory GenAI and ML engineering skills enforce strict shortlisting.
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Design, build, and fine-tune Generative AI models including Large Language Models, vision-language models, and diffusion models for product use cases.
Implement and manage retrieval-augmented generation (RAG) architectures and vector databases with embeddings for AI applications.
Collaborate with engineering teams to integrate AI/ML models into production and embedded/edge environments, using experiment tracking tools like MLflow or Weights & Biases.
Bachelor's or Master's degree in Computer Science, Data Science, Information Management/Systems, Data Analytics, or equivalent.
Minimum 7 years experience with Bachelor's degree OR minimum 5 years experience with Master's degree in Data Analytics, Data Modeling, or related fields.
Hands-on expertise with Large Language Models, Generative AI frameworks (e.g., LangChain), vector databases, and prompt engineering.
Must be available for at least 3 days per week onsite; full-time presence required in company facilities for office-based roles.
Experienced in applying traditional machine learning and deep learning techniques alongside cutting-edge Generative AI approaches.
Technically proficient in Python and statistical programming, with solid knowledge of experiment tracking and ML engineering fundamentals.
Able to work closely with cross-functional engineering teams to operationalize AI solutions in complex, production and embedded environments.