GenerativeAI

6 years of experience in Generative AI focusing on LLMs NLP techniques and financial applications.

Key Responsibilities:

• Generative AI Model Development: Develop advanced Generative AI models leveraging LLMs (e.g. GPTClaudeGeminiLLama) to automate and enhance decision-making report generation and analysis specifically within financial contexts.
• GenAI Ops: Implement GenAI Ops (Generative AI Operations) principles managing the AI lifecycle from data operations and model monitoring to maintenance and optimization. Ensure operational readiness and reliability of AI solutions.
• Human-in-the-Loop (HITL): Establish HITL feedback mechanisms to refine and validate AI-generated outputs. Collaborate with financial domain experts to improve model performance and ensure model accuracy relevance and alignment with business objectives.
• Retrieval-Augmented Generation (RAG): Integrate RAG techniques to enhance LLM performance by enabling the retrieval of up-to-date authoritative information from external knowledge sources. This is critical for providing accurate and reliable insights especially in financial applications.
• Deployment & Scalability: Lead the deployment of GenAI models in cloud environments ensuring scalability security and seamless integration with existing financial systems.

Experience:

• Proficiency in GenAI frameworks like LangChain LlamaIndex Hugging Face etc.
• Strong understanding of Generative AI deployment strategies including pilot programs technical assessments and governance planning.
• Expertise in GenAI Ops: managing the lifecycle of Generative AI models including model deployment monitoring versioning and optimization.
• Hands-on experience in Retrieval-Augmented Generation (RAG) to connect generative models to external data sources for improved performance and accuracy.
• Understanding of financial datasets and use cases including financial reporting risk management and fraud detection.
• Proficiency in Python with deep knowledge of machine learning frameworks (e.g. TensorFlow PyTorch scikit-learn pandas NumPy).
• Familiarity with cloud-based platforms like AWS Azure or Google Cloud for AI model deployment.
• Knowledge of MLOpsGenAIOps practices including version control experiment tracking and model monitoring.
• Strong communication skills with the ability to explain complex AI concepts to non-technical stakeholders.
• Analytical mindset with a focus on innovation and solving complex financial problems using AI.