Tech Risk -Analyst -Mumbai
Bangalore, Karnataka, India
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.
Responsibilities:
• Develop and implement machine learning algorithms, Generative AI models for various projects, with a focus on the financial and banking domain.
• 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.
• Work with a team of AI/ML engineers and data scientist to implement AI solutions, or AI agents, build MLOps Pipeline on cloud (AWS, Azure, or GCP) and on-prem setup along with CI/CD Pipelines.
• Work with data mining toolkits like NLP, Semantic Web, NLTK, and information retrieval libraries like Lucene, SOLR, Elastic.
• Work with version controls such as Git or bitbucket.
• Work with containerization and docker implementation and Kubernetes
• Optimize Gen AI models for improved accuracy.
• Testing and monitoring the AI models for accuracy and efficiency.
• Keep abreast of the latest AI/ML threats, vulnerabilities, and countermeasures.
• Communicate with a team and document the processes
• Stay up to date on emerging AI technologies, framework and methodologies.
Requirements:
• 1-3 years of hands-on experience in software development, with a focus on AI/ML, NLP, DL.
• Strong knowledge of AI/ML algorithms, Recommendation systems, Reinforcement Learning, Gen AI and AI Agents.
• Proficient in any one of the programming languages like Python or R, knowledge of frameworks such as scikit-learn, Keras, PyTorch, Tensorflow, etc., with an understanding of AI compliance.
• Ability to design and implement cloud solutions and ability to build MLOps pipelines on cloud solutions (AWS, MS Azure or GCP/On-premise hosting).
• Well-versed with various AI/ML libraries for managing Bias, Variance etc.
• Experience in FastAPI.
• Experience with MLOps Frameworks like Kubeflow, MLFlow, DataRobot, Airflow etc., experience with Docker and Kubernetes, OpenShift
• Having knowledge or an understanding of Open-Source Tools such as MLFlow and Fairlearn.
• Having an understanding of Gen AI prompt engineering techniques such as N-Shot, Chain of thoughts, Cove, etc.
• Ability to stay updated with the latest AI/ML security trends and technologies.
Equal employment opportunity information
KPMG India has a policy of providing equal opportunity for all applicants and employees regardless of their color, caste, religion, age, sex/gender, national origin, citizenship, sexual orientation, gender identity or expression, disability or other legally protected status. KPMG India values diversity and we request you to submit the details below to support us in our endeavor for diversity. Providing the below information is voluntary and refusal to submit such information will not be prejudicial to you.
Qualification:
• Bachelor’s degree in computer science or software engineering
• 1-3 years of AI/ML and python development.
* Salary range is an estimate based on our AI, ML, Data Science Salary Index 💰
Tags: Airflow AWS Azure Banking Bitbucket CI/CD Computer Science Computer Vision Data Mining DataRobot Deep Learning Docker Engineering FastAPI GCP Generative AI Git Keras Kubeflow Kubernetes LLMs Machine Learning MLFlow MLOps NLP NLTK Open Source Pipelines Privacy Prompt engineering Python PyTorch R RAG Reinforcement Learning Scikit-learn Security TensorFlow Testing
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