This article details the role and project scope for building AI product/service prototypes using machine learning. It covers core job responsibilities—prototyping, market segmentation, financial and business model development—and primary projects (T0–T3). It also outlines required qualifications and technical knowledge in probability, statistics, linear algebra, calculus, and tools like Pandas, Scikit Learn, TensorFlow and PyTorch for AI-driven offerings and implementation.
Responsibilities and Primary Projects
Role focus:
- Using Machine Learning to prototype an AI Product/Service. The primary responsibility is end-to-end prototyping of an AI product/service, reflected directly in project T0 (AI Product/Service Prototyping) and T3 (AI Product/Service Prototype Development).
- Using Machine Learning algorithms to perform Market Segmentation of the assigned Market Domain. This aligns with project T1, Large Scale Market Segmentation using Machine Learning and Data Analysis, emphasizing algorithmic approaches to segment markets at scale.
- Develop financial model and equation of the AI Product/Service Prototype. Financial equation development is an explicit responsibility tied to prototype viability and evaluation, forming a core part of the prototype lifecycle.
- Develop Practical Business Model of the AI Product/Service Prototype. Building a practical business model complements technical prototyping and financial modeling to ensure the prototype maps to realistic commercial outcomes.
Primary projects (directly tied to responsibilities):
- T0. AI Product/Service Prototyping — initial prototyping and concept validation.
- T1. Large Scale Market Segmentation using Machine Learning and Data Analysis — algorithmic segmentation work for the assigned market domain.
- T2. Analogy Bot Generator – Data Collection and Pre-processing — focused on gathering and preparing data inputs that support prototype functions.
- T3. AI Product/Service Prototype Development — iterative development and refinement of the prototype into a working artifact.
Qualifications and Knowledge Requirements
Candidate profile and technical foundation:
- Qualifications:
- B.Tech/M.Tech
- B.Sc/M.Sc
- BCA/MCA
- Knowledge Requirements:
- Mathematical foundations: Probability, Statistics, Linear Algebra, Calculus — necessary for model design, evaluation, and financial equation formulation.
- Tooling and frameworks: Pandas, Scikit Learn, TensorFlow, PyTorch — required for data processing, algorithm development, model training, and prototype construction.
Together, these qualifications and knowledge areas equip candidates to execute the listed primary projects (T0–T3) and meet the stated job responsibilities.
Conclusion
In summary, this role centers on using machine learning to prototype AI products and perform large-scale market segmentation while developing financial equations and practical business models. Primary projects T0–T3 map directly to prototyping, segmentation, data collection/pre-processing, and prototype development. Candidates with the listed qualifications and knowledge can execute these focused, interrelated tasks to deliver AI product/service prototypes effectively at scale.
