Responsibilities :

- Contribute to the development and optimization of enterprise-wide search systems and models.

- Design and implement algorithms to improve indexing, query relevance, and search accuracy.

- Support taxonomy, ontology, and metadata model creation for better search outcomes.

- Collaborate with business units (Loans, Insurance, Investments) to build AI-enabled search features.

- Conduct analysis of user behavior and system metrics to refine search performance.

- Work with engineers, product managers, and designers to deliver integrated search solutions.

- Develop production-grade ML systems for ranking, personalization, and recommendations.

- Participate in proof-of-concept initiatives with internal and external partners.

- Follow best practices in software engineering including CI/CD, testing, and monitoring.

- Keep abreast of emerging developments in AI/ML to apply them in practical solutions.

Requirements :

- Must have minimum 5+ years of hands-on experience in Data Science, Machine Learning, Applied AI, NLP, Deep Learning, or Generative AI solutions.

- Must have strong hands-on experience in Python programming, SQL, data analysis, feature engineering, model development, and production-grade ML applications.

- Must have experience working with Machine Learning and Deep Learning frameworks such as PyTorch, TensorFlow, Keras, Scikit-learn, or equivalent.

- Must have hands-on experience working on NLP, embeddings, semantic search, text classification, document understanding, recommendation systems, or similar AI/ML use cases.

- Must have experience working with Large Language Models (LLMs) such as GPT, Llama, Mistral, Claude, Gemini, Phi, or similar foundation models.

- Must have hands-on experience building or implementing RAG (Retrieval Augmented Generation) systems, vector search, knowledge retrieval, embeddings, chunking, indexing, or semantic retrieval solutions.

- Must have experience working with Git, CI/CD practices, production environments, and scalable AI/ML systems.