Data Scientist – AI Research & Enterprise Intelligence

Data Scientist – AI Research & Enterprise Intelligence

Department: Bancapp AI Lab

Experience: 2–4 Years

Employment Type: Full-Time

About the Role

Bancapp AI Lab is looking for passionate engineers and AI practitioners to build intelligent enterprise software for Banking, Financial Services, Insurance, and Enterprise Automation. As a Data Scientist, you will design and develop AI-driven solutions that combine Statistical Machine Learning, Machine Learning, Small Language Models (SLMs), Large Language Models (LLMs), Agentic AI, Retrieval-Augmented Generation (RAG), and Explainable AI to solve complex business problems.

This role combines AI research, software engineering, full-stack development, and enterprise system integration to deliver scalable, production-ready AI capabilities.

Key Responsibilities (KRAs)AI & Machine Learning

·      Design, develop, train, evaluate, and optimize Statistical Machine Learning and Machine Learning models.

·      Develop intelligent systems for anomaly detection, forecasting, recommendation, classification, clustering, NLP, document intelligence, and financial analytics.

·      Build explainable AI models with measurable accuracy, transparency, and reliability.

Language Models & Enterprise Intelligence

·      Build enterprise AI solutions using foundation LLMs.

·      Fine-tune and optimize domain-specific Small Language Models (SLMs).

·      Develop Retrieval-Augmented Generation (RAG) pipelines and enterprise knowledge assistants.

·      Improve retrieval quality, prompt engineering, context management, and response evaluation.

Agentic AI

·      Design and develop AI agents and multi-agent systems for enterprise workflow automation.

·      Implement planning, reasoning, memory, tool calling, orchestration, and autonomous task execution.

·      Integrate AI agents with enterprise applications, databases, APIs, messaging systems, and business workflows.

Full Stack AI Engineering

·      Develop backend services using Python, FastAPI, or Django.

·      Build AI-enabled web applications using Angular or React.

·      Develop REST APIs, authentication, enterprise integrations, and reusable AI services.

·      Deploy AI solutions using Docker, Git, CI/CD, and cloud or on-premise infrastructure.

Financial AI & Automation

·      Develop intelligent reconciliation, fraud detection, document intelligence, forecasting, process mining, and workflow automation solutions.

·      Integrate AI into enterprise business processes to improve operational efficiency and decision-making.

Continuous Learning & Innovation

·      Stay current with emerging AI technologies and evaluate their applicability to enterprise products.

·      Read and implement relevant research papers where beneficial.

·      Contribute to technical documentation, internal knowledge sharing, and innovation initiatives.

AI Engineering Principles

Every solution developed at Bancapp AI Lab should:

·      Solve real business problems.

·      Prefer explainable (white-box) AI wherever technically feasible.

·      Use LLMs and SLMs responsibly with appropriate guardrails.

·      Implement input validation, output verification, monitoring, and human oversight where required.

·      Be secure, scalable, reusable, auditable, and production-ready.

Required Technical SkillsProgramming

·      Python

·      SQL

·      Pandas

·      NumPy

·      Object-Oriented Programming

Artificial Intelligence

·      Statistical Machine Learning

·      Machine Learning

·      Deep Learning

·      NLP

·      Large Language Models (LLMs)

·      Small Language Models (SLMs)

·      Prompt Engineering

·      Retrieval-Augmented Generation (RAG)

·      AI Agents

·      Explainable AI (XAI)

Frameworks

·      PyTorch or TensorFlow

·      Scikit-learn

·      Hugging Face

·      LangChain

·      LangGraph

·      LlamaIndex

Full Stack & Engineering

·      FastAPI / Django

·      Angular or React

·      PostgreSQL / MySQL

·      REST APIs

·      Docker

·      Git

·      CI/CD

Qualifications

Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Software Engineering, Mathematics, or a related technical discipline.

Domain expertise in Banking, Payments or Reconciliation is MUST.

What We Look For

Engineers who enjoy building intelligent systems, learning continuously, solving challenging enterprise problems, and delivering production-ready AI solutions with a strong focus on explainability, quality, and business impact.