Data Scientist

Must have

• 3–4 years of experience building AI/ML applications with strong Python and software engineering skills.

• Strong proficiency in Python.

• Hands-on experience with:

o Understanding of AI system design, evaluation, monitoring, and performance

optimization.

o Experience developing, deploying, and maintaining production AI

applications.

o Strong software engineering fundamentals, including clean code, modular design,

debugging, and testing.

o RAG architectures

• Experience with machine learning frameworks such as TensorFlow, PyTorch, or Scikit-learn.

• Good understanding of NLP concepts.

• Familiarity with API development and integration.

• Ability to work independently as well as in collaborative team environments.

Good to have

• Experience with vector databases such as Pinecone, Weaviate, FAISS, ChromaDB.

• Knowledge of cloud platforms such as AWS, Azure, or GCP.

• Experience with Docker, and deployment pipelines.

• Exposure to fintech products, platforms, or domain-specific use cases.

Roles & Responsibilities

• Design scalable, reliable AI systems with a focus on production readiness, maintainability, and performance.

• Perform root cause analysis and debugging of AI pipelines to improve accuracy, consistency, and reliability.

• Develop automated evaluation, validation, and testing strategies for AI applications.

• Design reliable LLM workflows using structured outputs, validation, retrieval, and business rules.

• Create and maintain RAG pipelines and AI agent frameworks.

• Perform data analysis, model evaluation, and performance optimization.

• Develop APIs and integrate AI solutions into existing products and platforms.

• Collaborate with cross-functional teams including engineering, and business stakeholders to define AI use cases and deliver solutions.

• Monitor, troubleshoot, and continuously improve deployed AI systems.