ML Engineering Lead at Saris AI tackling multi - modal AI systems in banking. Drive technical direction and build high - performing teams in an early - stage startup environment.
About the role
- Lead ML validation team for Generative AI models at TD Bank. Oversee evaluation, testing, and compliance with industry standards and regulatory requirements.
Responsibilities
- Lead R&D in the GenAI/Agentic/LLM evaluation and testing areas.
- Lead a team of Machine Learning Scientists to perform validation of complex AI/ML models, particularly Generative AI, LLMs / NLP and Deep Learning models.
- Validate AI/ML models, particularly Generative AI and Deep Learning models.
- Recommend the approval of models or other corrective actions based on the independent validation.
- Lead and support a team of model validators.
- Ensure performance objectives are set for all staff and that performance feedback is provided on a regular basis.
- Communicate group objectives and strategies and align group activities in support of business objectives.
- Support employee development activities, coach and support direct reports in meeting their personal development objectives.
- Assume a leadership role in developing standards and procedures for vetting and validation of Generative AI, NLP and diverse Deep Learning models that are compliant with the Bank’s internal Model Risk Policy, adhere with industry and academic best practices, and meet regulatory requirements.
- Respond to requests from both Canadian and U.S. regulators, internal and external audit in their review/audit of models and vetting/validation process and procedures.
Requirements
- Advanced quantitative and AI / ML skills with post-secondary degree in one or more of the following areas: computer science, machine learning / AI, engineering, statistics, mathematics, etc.
- 4+ years of experience in either developing or validating Generative AI, LLMs, NLP and other Deep Learning models; and 2+ years of experience in leading a small team of machine learning scientists.
- In-depth knowledge of AI/ML methodologies, concepts and theory including Generative AI, Deep Learning, modern Natural Language Processing (NLP), Retrieval-Augmented Generation (RAG), Transformers, Diffusion models, etc.
- Experience with Deep Learning and Generative AI technology stacks and libraries such as PyTorch, PromptFlow, LangChain, HuggingFace, etc.
- Motivated to stay up to date with the latest advancements in Generative AI, machine learning, and cloud technologies.
- Proficient in one or more scripting/programming languages such as Python.
- Familiarity with cloud platforms (e.g., Azure, AWS) and big data technologies (e.g., PySpark, Hadoop).
- Familiarity with Data Structures, Algorithm design, and principles of Object-Oriented Programming (OOP).
- Knowledge of machine learning explain-ability/interpretability algorithms.
- Excellent verbal and written communication skills.
- Strong critical and analytic thinking skills.
- Excellent time / project management and multitasking skills with minimal supervision.
- Ability to work independently and collaboratively in a fast-paced, dynamic environment.
Benefits
- Health and well-being benefits
- Savings and retirement programs
- Paid time off
- Banking benefits and discounts
- Career development
- Reward and recognition programs
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