Job Description

Area(s) of responsibility

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., GPT,Claude,Gemini,LLama) 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 MLOps,GenAIOps 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.