AI Research Lead

2 days ago

Pune, Maharashtra, India beBeeDataScience Full time ₹ 18,57,000 - ₹ 25,16,000

Job Title: AI Research Lead

Job Description:

A graduate degree in a quantitative field such as computer science, statistics, applied mathematics, machine learning, or related disciplines is required for this role.

This position involves leading a team of Data Engineers, Analysts, and Data Scientists in carrying out project activities. Key skills and qualifications include strong programming skills in Python with expertise in Python's numerical, data analysis, and AI frameworks such as NumPy, Pandas, Scikit-learn, etc.

Key Skills and Responsibilities:
  • Experience with Large Language Models (LLMs) including Llama, T5, Falcon, Langchain, or similar frameworks.
  • Familiarity with the evolution history of Natural Language Processing (NLP), training data creation, setup, and fine-tuning is essential.
  • Ability to interpret research papers and architecture diagrams of Language Models is crucial.
  • Comfortable with techniques like LORA, RAG, Instruct fine-tuning, Quantization, etc.
Leadership Responsibilities:
  • Lead a team of Data Engineers, Analysts, and Data Scientists in carrying out project activities.
  • Connect with internal/external stakeholders to understand business requirements.
  • Coordinate with relevant parties to gather data artifacts, anecdotes, and hypotheses.
  • Create project plans and sprints for milestones and deliverables.
  • Spin virtual machines, create and optimize clusters for Data Science workflows.
  • Develop data pipelines to ingest data effectively.
  • Ensure data quality through proactive checks and resolve gaps.
  • Conduct exploratory data analysis, feature engineering, and define performance metrics before running ML/DL algorithms.
  • Research existing solutions before building ML models.
  • Create optimized data models for efficient querying.
  • Run ML/DL algorithms for business goals.
  • Optimize and validate models for scalability.
  • Develop light applications, simulators, and scenario builders for business consumption.
  • Integrate and operationalize models in client ecosystems.
  • Document project artifacts and log failures.
  • Measure and articulate impact on business metrics.

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