About KPMG in India
KPMG entities in India are professional services firm(s). These Indian member firms are affiliated with KPMG International Limited. KPMG was established in India in August 1993. Our professionals leverage the global network of firms, and are conversant with local laws, regulations, markets and competition. KPMG has offices across India in Ahmedabad, Bengaluru, Chandigarh, Chennai, Gurugram, Jaipur, Hyderabad, Jaipur, Kochi, Kolkata, Mumbai, Noida, Pune, Vadodara and Vijayawada.
KPMG entities in India offer services to national and international clients in India across sectors. We strive to provide rapid, performance-based, industry-focused and technology-enabled services.
Requirements-
Strong proficiency in AI/ML concepts, algorithms, and techniques.
Extensive experience with machine learning frameworks such as TensorFlow, PyTorch, or scikit-learn.
Proficiency in programming languages such as Python, and familiarity with libraries like NumPy, Pandas, and SciPy.
Familiarity with generative AI models such as GANs (Generative Adversarial Networks), VAEs (Variational Autoencoders), or transformers (e.g., GPT, BERT) is a plus.
Experience with data preprocessing, feature engineering, and model evaluation techniques.
Strong understanding of version control systems like Git.
Experience with MLOps practices, including CI/CD for machine learning, model versioning, and monitoring.
Excellent problem-solving skills and the ability to design and implement scalable, maintainable solutions.
Strong communication and collaboration skills for effectively working with cross-functional teams, including data scientists, software engineers, and product managers.
Qualification
Bachelor’s degree in Engineering (B.Tech/BE), Computer Applications (MCA), or a related field is required.
A Master’s or Ph.D. in a Data Science, Machine Learning/AI or relevant field is highly preferred and will be considered a strong advantage.
Experience with cloud platforms such as AWS, Azure, or Google Cloud for deploying AI/ML solutions.
Familiarity with model interpretability tools such as LIME and SHAP.
Contributions to open-source projects or active participation in the AI/ML community.
Certifications in relevant technologies or cloud platforms.
Experience with Agile/Scrum development methodologies.
