Senior GenAI Specialist - Finance

Role Overview

We are seeking a highly skilled and passionate Senior GenAI Specialist to join our team in Mississauga, Canada. You will play a key role in designing, developing, and implementing cutting-edge Generative AI (GenAI) solutions, including exploring and applying advanced concepts like Agentic AI, within our financial operations. This role offers a challenging opportunity to contribute to impactful projects, leveraging deep technical expertise in GenAI, LLMs, RAG pipelines, vector databases, chatbot development, and related technologies. You will collaborate with engineers and stakeholders and drive innovation by applying GenAI to solve complex financial problems.

Responsibilities

• Design, develop, and implement GenAI solutions for various financial applications, including personalized recommendations, risk assessment, fraud detection, and automated reporting. Explore and experiment with advanced GenAI concepts like Agentic AI.
• Design and implement intelligent chatbots.
• Process and analyze large datasets of structured and unstructured financial data.
• Architect and implement efficient RAG pipelines, leveraging tools like LlamaIndex and LangChain.
• Develop and refine advanced prompting strategies for LLMs.
• Test, evaluate, and analyze the performance of LLM and other GenAI models.
• Collaborate closely with engineering teams to deploy and maintain GenAI models in production environments, including containerization, CI/CD pipelines, and cloud infrastructure management.
• Communicate effectively with business stakeholders.
• Stay up to date with the latest advancements in GenAI research and development, including areas like Agentic AI.

Required Skills and Qualifications

• Master's degree or PhD in Computer Science, Engineering, Statistics, or a related field.
• 5-8 years of experience in AI/ML development, with a proven track record of building and deploying sophisticated GenAI applications.
• Deep understanding of GenAI models and architectures, including transformers, LLMs (e.g., Llama, Gemini, GPT-4), GANs, and diffusion models. Familiarity with Agentic AI concepts.
• Extensive experience with prompt engineering, fine-tuning LLMs, and evaluating their performance.
• Expert-level Python programming skills and proficiency with relevant libraries (e.g., Transformers, LangChain, TensorFlow, PyTorch, Pandas, NumPy, Scikit-learn, Flask/Django, LlamaIndex).
• Experience with vector databases (e.g., Pinecone, Weaviate, Chroma, Faiss, PostgreSQL with vector extensions) and implementing RAG pipelines using tools like LlamaIndex and LangChain.
• Strong software engineering skills, including containerization (Docker, Kubernetes), CI/CD pipelines, and cloud infrastructure management (AWS, Azure, GCP).
• Strong analytical, problem-solving, and communication skills.
• Experience with MLOps principles and tools.
• Excellent collaboration skills.

Preferred Qualifications

• Experience with financial data and applications, particularly in areas like fraud detection, risk management, or personalized financial advice.
• Strong understanding of financial markets and instruments.
• Familiarity with chatbot development frameworks and best practices, including conversational AI design and natural language understanding (NLU).
• Experience leading or contributing to complex data science or AI/ML projects in a fast-paced environment.
• Publications or presentations at conferences related to AI/ML or GenAI.
• Experience with data visualization and reporting tools (e.g., Tableau, Power BI, matplotlib, seaborn).
• Experience with SQL and NoSQL databases.

Technology Stack

• Programming Languages: Python (expert proficiency required), SQL
• Python Packages: Transformers, LangChain, LlamaIndex, TensorFlow, PyTorch, Pandas, NumPy, Scikit-learn, Flask/Django, and other relevant data science, machine learning, and web development libraries.
• Deep Learning Frameworks: TensorFlow, PyTorch
• LLMs: Llama, Gemini, GPT-4, and other advanced LLMs.
• Vector Databases: Pinecone, Weaviate, Chroma, Faiss, PostgreSQL with vector extensions (pgvector).
• Cloud Platforms: AWS, Azure, GCP
• MLOps Tools: MLflow, Kubeflow, or similar.
• Containerization: Docker, Kubernetes
• CI/CD Tools: GitHub Actions, Jenkins, or similar.
• Version Control: Git
• Data Visualization & Reporting: Tableau, Power BI, matplotlib, seaborn.
• Databases: SQL and NoSQL databases.

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