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TD Newton
About the role
Roles & responsibilities
Here are some of the key responsibilities of AI Research Scientist:
1.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).
2.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.
3.Agentic AI Systems: Develop and design autonomous AI systems that
exhibit agentic behavior, capable of making independent decisions and
adapting to dynamic environments.
4.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.
5.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.
6.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.
7.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.
8.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.
9.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.
10.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.
Mandatory technical & functional skills
·Strong programming skills in Python and frameworks like PyTorch or
TensorFlow.
·Scientific understanding and In depth knowledge on Deep Learning - CNN,
RNN, LSTM, Transformers LLMs ( BERT, GEPT, etc.) and NLP algorithms. Also,
familiarity with frameworks like Langgraph/CrewAI/Autogen to
develop, deploy and evaluate AI agents.
·Ability to test and deploy open source LLMs from Huggingface, Meta- LLaMA
3.1, BLOOM, Mistral AI etc.
Hands-on ML platforms offered through GCP : Vertex AI or Azure : AI Foundry or AWS SageMaker
Preferred technical & functional skills
—Ability to create detailed technical architecture with scalability in
mind for the AI solutions. Ability to explore hyperscalers and provide
comparative analysis across different tools.
—Cloud computing experience, particularly with Google/AWS/Azure Cloud
Platform, is essential. With strong foundation in understating Data
Analytics Services offered by Google/AWS/Azure ( BigQuery/Synapse)
—Large scale deployment of GenAI/DL/ML projects, with good understanding
of MLOps /LLM Ops
Key behavioral attributes/requirements
—Ability to mentor junior developers
—Ability to own project deliverables, not just individual tasks
Understand business objectives and functions to support data needs