We are seeking a highly motivated and enthusiastic Data Scientist with 5-8 years of experience to join our dynamic team. The ideal candidate will have a strong background in AI/ML analytics and a passion for leveraging data to drive business insights and innovation.
Key Responsibilities:
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Develop and implement machine learning models and algorithms.
Work closely with project stakeholders to understand requirements and translate them into deliverables.
Utilize statistical and machine learning techniques to analyze and interpret complex data sets.
Stay updated with the latest advancements in AI/ML technologies and methodologies.
Collaborate with cross-functional teams to support various AI/ML initiatives.
Bachelor's degree in computer science, Data Science, or a related field.
Strong understanding of machine learning, deep learning and Generative AI concepts.
Preferred Skills:
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Experience in machine learning techniques such as Regression, Classification, Predictive modeling, Clustering, Deep Learning stack using python.
Experience with cloud infrastructure for AI/ ML on AWS(Sagemaker, Quicksight, Athena, Glue).
Expertise in building enterprise grade, secure data ingestion pipelines for unstructured data(ETL/ELT) - including indexing, search, and advance retrieval patterns.
Proficiency in Python, TypeScript, NodeJS, ReactJS (and equivalent) and frameworks. (e.g., pandas, NumPy, scikit-learn, SKLearn, OpenCV, SciPy), Glue crawler, ETL.
Experience with data visualization tools (e.g., Matplotlib, Seaborn, Quicksight).
Knowledge of deep learning frameworks (e.g., TensorFlow, Keras, PyTorch).
Experience with version control systems (e.g., Git, CodeCommit).
Strong knowledge and experience in Generative AI/ LLM based development.
Strong experience working with key LLM models APIs (e.g. AWS Bedrock, Azure Open AI/ OpenAI) and LLM Frameworks (e.g. LangChain, LlamaIndex).
Knowledge of effective text chunking techniques for optimal processing and indexing of large documents or datasets.
Proficiency in generating and working with text embeddings with understanding of embedding spaces and their applications in semantic search and information retrieval.
Experience with RAG concepts and fundamentals (VectorDBs, AWS OpenSearch, semantic search, etc.), Expertise in implementing RAG systems that combine knowledge bases with Generative AI models.
Knowledge of training and fine-tuning Foundation Models (Athropic, Claud, Mistral, etc.), including multimodal inputs and outputs.
Good to have Skills:
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Knowledge and Experience in building knowledge graphs in production.
Understanding of multi-agent systems and their applications in complex problem-solving scenarios.
Interview Process
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* 3 Technical Rounds
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