AI /ML - Executive - Mumbai

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.

•       Develop and implement machine learning algorithms for various projects, with a focus on the financial and banking domain.

•       Utilize expertise in bias and variance algorithms to enhance model accuracy and performance

•       Develop and apply techniques for Explainability, Privacy, and fairness in AI models and Generative AI.

•       Use case development including building AI applications, such as Deep learning, LLM/RAG/finetuning, NLP, computer vision and pattern recognition.

•       Design and implement cloud solutions, build MLOps Pipeline on cloud (AWS, Azure, or GCP) and on-prem setup along with CI/CD Pipelines.

•       Design and develop NLP-based applications and system using DL, neural networks and chatbots or AI agents.

•       Work with data mining toolkits like NLP, Semantic Web, NLTK, and information retrieval libraries like Lucene, SOLR.

•       Build CI/CD pipelines orchestration by GitLab CI, GitHub Actions, Circle CI, Airflow or similar tools

•       Data science model review, run the code refactoring and optimization, containerization, deployment, versioning, Testing and Automation and monitoring of Model’s quality

•       Deploy the Responsible AI technical framework to Cloud.

•       Build and maintain databases such as NoSQL dbs, Vector Dbs, and object storages.

•       Identify ways of embedding AI/ML services into enterprise architecture seamlessly.

•       Keep abreast of the latest AI/ML threats, vulnerabilities, and countermeasures.

•       Monitor and evaluate model performance, making necessary adjustments to improve accuracy and efficiency.

•       Communicate with a team of data scientists, data engineers and architect, document the processes

•       Stay up to date on emerging AI technologies, framework and methodologies.