Wipro-Data Scientist-Gen Ai

Job Summary: We are seeking a highly skilled and versatile Machine Learning

Engineer who embodies the rare combination of a strong software engineer and ML

exposure with experience in designing, developing, and maintaining robust, scalable, and

efficient software applications using Python, with a strong emphasis on Object-Oriented

Programming principles to manage hyperparameters, encapsulate evaluation metrics, and

create controlled interfaces for model wrappers. You will be instrumental in designing,

developing, deploying, and maintaining our core AI-powered products and features. This

demands a blend of analytical rigor, coding prowess, architectural foresight, and a deep

understanding of the entire machine learning lifecycle, from data exploration and model

development to deployment, monitoring, and continuous improvement.

Key Responsibilities:

• Coding: Write clean, efficient, and well-documented Python code adhering to OOP

principles (encapsulation, inheritance, polymorphism, abstraction). Experience with

Python and related libraries (e.g., TensorFlow, PyTorch, Scikit-Learn). They are

responsible for the entire ML pipeline, from data ingestion and preprocessing to

model training, evaluation, and deployment

• End-to-End ML Application Development: Design, development, and deployment

of machine learning models and intelligent systems into production environments,

ensuring they are robust, scalable, and performant.

• Software Design & Architecture: Apply strong software engineering principles to

design and build clean, modular, testable, and maintainable ML pipelines, APIs, and

services. Contribute significantly to the architectural decisions for our ML platform

and applications.

• Data Engineering for ML: Design and implement data pipelines for feature

engineering, data transformation, and data versioning to support ML model training

and inference.

• MLOps & Productionization: Establish and implement best practices for MLOps,

including CI/CD for ML, automated testing, model versioning, monitoring

(performance, drift, bias), and alerting systems for production ML models.

• Performance & Scalability: Identify and resolve performance bottlenecks in ML

systems. Ensure the scalability and reliability of deployed models under varying load

conditions.

Internal - General

Use

• Documentation: Create clear and comprehensive documentation for ML models,

pipelines, and services.

Required Qualifications:

• Education: Masters degree in computer science, Machine Learning, Data Science,

Electrical Engineering, or a related quantitative field.

• Experience: 5+ years of professional experience in Machine Learning Engineering,

Software Engineering with a strong ML focus, or a similar role.

• Must have Programming Skills: Expert-level proficiency in Python, including

experience with writing production-grade, clean, efficient, and well-documented code.

Experience with other languages (e.g., Java, Go, C++) is a plus.

• Strong Software Engineering Fundamentals: Deep understanding of software

design patterns, data structures, algorithms, object-oriented programming, and

distributed systems.

• Good to have Machine Learning Expertise:

• Solid theoretical and practical understanding of various machine learning

algorithms

• Proficiency with ML frameworks such as PyTorch, Scikit-learn.

• Experience with feature engineering, model evaluation metrics, and

hyperparameter tuning

• Data Handling: Experience with SQL and NoSQL databases, data warehousing

concepts, and processing large datasets.

• Problem-Solving: Excellent analytical and problem-solving skills, with a pragmatic

approach to delivering solutions.

• Communication: Strong verbal and written communication skills, with the ability to

explain complex technical concepts to both technical and non-technical audiences.

Preferred Qualifications:

• Experience with big data technologies (e.g., Spark, Hadoop, Kafka).

• Contributions to open-source projects or a strong portfolio of personal projects.Role & responsibilities