SA1/Sr. Engineer - AI Research Scientist at KPMG India

Hyderabad, Telangana, India -

Full Time

Start Date

Immediate

Expiry Date

17 May, 26

Salary

0.0

Posted On

16 Feb, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Generative AI, Model Architecture, Deep Learning, Natural Language Processing, Machine Learning, Multimodal Development, Agentic AI Systems, Algorithm Optimization, Data Preprocessing, Feature Engineering, Model Evaluation, Technical Mentorship, Parallelization, Distributed Computing, Hardware Acceleration

Industry

Business Consulting and Services

Description

Roles & responsibilities Here are some of the key responsibilities of AI Research Scientist: Research and Development: Conduct original research on generative AI models, focusing on model architecture, training methodologies, fine-tuning techniques, and evaluation strategies. Maintain a strong publication record in top-tier conferences and journals, showcasing contributions to the fields of Natural Language Processing (NLP), Deep Learning (DL), and Machine Learning (ML). Multimodal Development: Design and experiment with multimodal generative models that integrate various data types, including text, images, and other modalities to enhance AI capabilities. Develop POCs and Showcase it to the stakeholders. Agentic AI Systems: Develop and design autonomous AI systems that exhibit agentic behavior, capable of making independent decisions and adapting to dynamic environments. Model Development and Implementation: Lead the design, development, and implementation of generative AI models and systems, ensuring a deep understanding of the problem domain. Select suitable models, train them on large datasets, fine-tune hyperparameters, and optimize overall performance. Algorithm Optimization: Optimize generative AI algorithms to enhance their efficiency, scalability, and computational performance through techniques such as parallelization, distributed computing, and hardware acceleration, maximizing the capabilities of modern computing architectures. Data Preprocessing and Feature Engineering: Manage large datasets by performing data preprocessing and feature engineering to extract critical information for generative AI models. This includes tasks such as data cleaning, normalization, dimensionality reduction, and feature selection. Model Evaluation and Validation: Evaluate the performance of generative AI models using relevant metrics and validation techniques. Conduct experiments, analyze results, and iteratively refine models to meet desired performance benchmarks. Technical Mentorship: Provide technical leadership and mentorship to junior team members, guiding their development in generative AI through work reviews, skill-building, and knowledge sharing. Documentation and Reporting: Document research findings, model architectures, methodologies, and experimental results thoroughly. Prepare technical reports, presentations, and whitepapers to effectively communicate insights and findings to stakeholders. Continuous Learning and Innovation: Stay abreast of the latest advancements in generative AI by reading research papers, attending conferences, and engaging with relevant communities. Foster a culture of learning and innovation within the team to drive continuous improvement.

Responsibilities

The role involves conducting original research and development on generative AI models, focusing on architecture, training, and evaluation, while also designing and experimenting with multimodal generative models. Responsibilities include leading the design and implementation of these systems and optimizing algorithms for efficiency and scalability.