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Role Overview:
**Responsibilities:
**
- Lead research and development of new and innovative techniques to solve Artificial intelligence/Machine learning problems, supporting strategic business goals within the domain of Deep Learning, Generative AI, Natural language processing, Responsible AI, and Model Optimization.
- Manage a team of AI engineers and scientists responsible for establishing and implementing strategies to support Vanguard's AI vision, developing proprietary AI solutions, and identifying themes for AI Research and development.
- Engage with Vanguard leadership to understand and probe business processes to develop hypotheses and own the AI research agenda; bring structure to requests and translate them into an ML approach.
- Build, leverage internal assets, partner with external solution providers and vendor solutions to mature the use of advanced AI techniques and technologies and drive scalable business outcomes.
- Drive large-scale R\&D initiatives, managing resources, timelines, and risks effectively.
- Monitor and report on AI R\&D progress, trends, and competitive landscape to senior management.
- Ensure timely delivery of AI Research prototypes, maintaining a balance between innovation, quality, and efficiency.
- Establish and maintain partnerships with academic institutions and industry partners to keep up with cutting-edge advancements in AI.
- Recruit, hire, train, and develop top AI talent, building a diverse and inclusive team.
**Qualifications:
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- PhD or Master in a relevant discipline such as Computer Science, Cognitive Science, Mathematics, Statistics, Physics, Electrical \& Computer Engineering.
- At least 10 years of experience in AI research in industry or academic setting
- A proven record of AI Research leadership roles or similar capacity.
- Strong expertise in various AI/ML concepts and paradigm. Strong expertise in at least two or more of the following areas: Speech Recognition, Natural Language Processing, Reinforcement Learning, Knowledge Graph, Time-Series Analysis, or Generative AI.
- Experience with machine learning development lifecycle and AI/ML methods such as Transformers, Diffusion Models, SHAP, LLM and GenAI etc.
- Strong software engineering capabilities and hands-on experience with various machine learning and deep learning frameworks including numpy, scikit-learn, keras, PyTorch and Tensorflow
- A strong understanding of the real-world advantages and drawbacks of various algorithms and the ability to measure success.
- Ability to write clean, understandable code that follows leading industry standards and practices and is well-documented, and to build easily reproducible models.
How We Work
