We are seeking a skilled and passionate GenAI Engineer to join our team in Mississauga, Canada and contribute to developing cutting-edge Generative AI (GenAI) applications within our financial operations. This role offers an exciting opportunity to gain practical experience with GenAI, LLMs, RAG pipelines, vector databases, and chatbot development using Python. You will work alongside senior data scientists and engineers, contributing to the end-to-end implementation of innovative solutions leveraging AI to transform financial processes. Strong Python skills, analytical abilities, experience with prompt engineering, and a keen interest in GenAI are essential.
Responsibilities- Contribute to the development and fine-tuning of large language models (LLMs) and other GenAI models for financial applications.
- Assist in designing and implementing intelligent chatbots.
- Process and analyze structured and unstructured financial data to train and optimize GenAI models.
- Implement and optimize Retrieval Augmented Generation (RAG) pipelines.
- Develop and refine effective prompting strategies for LLMs, optimizing their performance for specific financial tasks.
- Test, evaluate, and analyze the performance of LLMs and other GenAI models, providing insights and recommendations for improvement.
- Contribute to model evaluation and monitoring.
- Collaborate with other data scientists, engineers, and business stakeholders.
- Participate in the end-to-end implementation of GenAI projects.
- Learn and apply best practices for GenAI development.
- Bachelor's or master's degree in a relevant field.
- 2-5 years of experience in data science or a related field, with a demonstrable interest and some practical experience in GenAI.
- Understanding of GenAI models and architectures.
- Good Python programming skills and familiarity with relevant libraries.
- Experience with prompt engineering and optimizing LLM outputs.
- Strong analytical and problem-solving skills.
- Familiarity with deep learning frameworks is a plus.
- Understanding of NLP and text processing techniques is desirable.
- Experience with cloud computing platforms is a plus.
- Excellent communication and collaboration skills.
- Experience with financial data and applications.
- Familiarity with chatbot development frameworks.
- Experience with vector databases.
- Contributions to open-source projects related to AI/ML or GenAI.
- Programming Languages: Python (good proficiency required)
- Python Packages: Relevant data science and GenAI packages (e.g., Pandas, NumPy, Scikit-learn, Transformers, LangChain).
- Deep Learning Frameworks: TensorFlow, PyTorch (familiarity is a plus)
- LLMs: Exposure to Llama, Gemini, GPT-4, or other LLMs.
- Vector Databases: PostgreSQL with vector extensions, other vector databases are a plus.
- Cloud Platforms: AWS, Azure, GCP (experience is a plus).
- Version Control: Git
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Job Family Group:
Technology------------------------------------------------------
Job Family:
Applications Development------------------------------------------------------
Time Type:
Full time------------------------------------------------------
Most Relevant Skills
Please see the requirements listed above.------------------------------------------------------
Other Relevant Skills
For complementary skills, please see above and/or contact the recruiter.------------------------------------------------------
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Citi Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Citi and has not been reviewed or approved by Citi.
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Healthcare Strength — Benefits coverage is positioned as comprehensive, including health, dental, and vision insurance plus on-site clinics, prescription drug support, and disability coverage. Family-building support such as fertility assistance is described as a notable differentiator within the overall package.
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Retirement Support — Retirement benefits are framed as strong, highlighted by a 401(k) with matching and additional plan options like a Roth 401(k). Financial support is reinforced through discounts and broader financial guidance resources tied to the benefits ecosystem.
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Wellbeing & Lifestyle Benefits — Wellbeing support extends beyond insurance through programs like an Employee Assistance Program, counseling/legal resources, and gym or wellness reimbursement. These offerings increase the perceived total rewards value even when cash compensation sentiment varies by role.
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