Data Scientist

Data Scientist

Key Responsibilities:

• Design, develop, and deploy machine learning models, including advanced architectures such as

  Large Language Models (LLMs).

• Collaborate with cross-functional teams to build scalable AI-driven solutions using robust software engineering principles.
• Leverage Python and object-oriented programming techniques to create efficient, maintainable, and production-grade ML pipelines.
• Work with popular machine learning frameworks such as

  TensorFlow

  ,

  PyTorch

  , and

  Scikit-learn

  for model development and experimentation.
• Integrate

  AI/ML models

  into applications using

  RESTful APIs

  and ensure performance, scalability, and reliability.
• Utilize tools such as

  Hugging Face

  ,

  LangChain

  , and

  OpenAI APIs

  to build and fine-tune NLP and generative AI models.
• Implement and manage containerized services using

  Docker

  , enabling smooth deployment and reproducibility of ML solutions.
• Apply best practices in version control (Git) and software development lifecycles to support collaborative and agile workflows.
• Design, develop, and maintain relational databases (e.g.,

  MySQL

  ,

  PostgreSQL

  ,

  Oracle

  ) to support data-driven applications.
• Contribute to the design and implementation of microservices-based architectures for AI/ML systems.
• Understand and apply agent communication protocols and multi-agent system design (e.g., MCP) where relevant to AI applications.
• Analyze large datasets, derive actionable insights, and drive continuous model improvements.

Required Skills and Qualifications:

• 4+ years of experience in

  data science, machine learning, or backend development roles.

• Proficiency in

  Python

  and strong foundations in

  object-oriented programming

  .
• Hands-on experience with

  machine learning model lifecycle

  — from training to production deployment and monitoring.
• Familiarity with

  scalable system design and microservices architecture.

• Excellent problem-solving skills, attention to detail, and a strong understanding of data structures and algorithms.
• Effective communication and collaboration skills, with the ability to work in a dynamic team environment.

Preferred Qualifications:

• Generative AI Expertise:

  Practical experience and in-depth knowledge of Generative AI technologies, including Large Language Models (LLMs) such as GPT, LLaMA, and their real-world applications.

• Prompt Engineering & Model Optimization:

  Proficiency in prompt engineering, fine-tuning LLMs, and evaluating the performance and effectiveness of AI/ML models.

• AI System Architecture:

  Familiarity with orchestration frameworks and agent-based system architectures for building modular and intelligent AI solutions.

• Advanced AI Tooling:

  Experience working with and integrating state-of-the-art AI tools, platforms, and APIs into scalable systems.

• Innovation Mindset:

  Demonstrated ability to creatively apply AI/ML — including Generative AI and LLMs — to solve complex problems and deliver business value, especially in the

  payment technology

  or

  fintech

  domains.

• Cross-Functional Collaboration:

  Strong communication skills with a proven track record of working effectively in cross-disciplinary teams, including product, engineering, and data stakeholders.

• Responsible AI Practices:

  Knowledge of ethical AI principles, including model interpretability, fairness, responsible AI deployment, and ongoing model monitoring.