Senior - Internal Audit

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, which reflect a shared knowledge of global and local industries and our experience of the Indian business environment.

Skills and Expertise: The consultant should have expertise in the following:

 1.      Designing, developing, and deploying AI-driven solutions using Python.

 2.      Developing and deploying Generative AI models for tasks such as text generation, image synthesis, or data augmentation.

 3.      Designing workflows to fine-tune Large Language Models (LLMs) like GPT or BERT for specific use cases.

 4.      Experience with Natural Language Processing (NLP) techniques and libraries such as spaCy, NLTK, or Hugging Face Transformers.

 5.      Proficiency in computer vision techniques and libraries such as OpenCV, TensorFlow, or PyTorch.

 6.      Implementing and optimizing AI algorithms for performance and scalability.

 7.      Knowledge of reinforcement learning and its applications.

 8.      Developing custom API endpoints for model deployment and real-time data processing. Ensuring API security and scalability for handling large volumes of requests efficiently.

 9.      Integrating third-party APIs to enhance AI functionality (e.g., speech-to-text, language translation, or image recognition).

 10.  Utilizing deep learning frameworks such as PyTorch or TensorFlow for building neural networks.

 11.  Strong understanding of AI/ML concepts, including frameworks like scikit-learn and TensorFlow. Experience with Machine Learning libraries for advanced modeling.

 12.  Experience with AI model deployment and serving using tools like Docker, Kubernetes, or cloud services (AWS, Azure, GCP).

 13.  Collaborating with data scientists and analysts to integrate machine learning models into production.

 14.  Hands-on experience with Python, knowledge of Data Analytics to proficiently write and optimize scripts.

 15.  Writing complex SQL queries to extract, transform, and analyze data from relational/non-relational databases.

 16.  Proficiency in version control tools such as Git.

 17.  Knowledge of data visualization tools like Power BI for creating dashboards and reports (good to have).

 18.  Exposure to ServiceNow for IT service management and automation (good to have).

 19.  Familiarity with Microsoft Azure cloud services for AI and data processing (good to have).

 20.  Good communication and presentation skills.

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