• Strong experience in developing and implementing large language models (LLMs) and generative AI solutions

• Proficiency in building LLM pipelines using foundational models, retrieval-augmented generation (RAG) techniques, and external API integrations

• Expertise in Python programming and relevant libraries such as TensorFlow, PyTorch, Hugging Face Transformers, and LangChain

• Experience with natural language processing (NLP) techniques and frameworks

• Familiarity with vector databases and embedding models for efficient information retrieval

• Knowledge of cloud platforms (e.g., AWS, Azure, GCP) for deploying and scaling AI solutions preferred

LLM Pipeline Risk Assessment - Preferred but not a must:

• Ability to identify and mitigate potential risks in LLM pipelines, including:

• Data privacy and security concerns

• Bias and fairness issues in model outputs

• Hallucination and factual accuracy problems

• Prompt injection vulnerabilities

• Model drift and performance degradation over time

• Experience in implementing safeguards and monitoring systems to ensure responsible AI deployment

Domain Knowledge - Preferred but not a must:

• Understanding of the banking and financial services industry, including regulatory requirements and compliance standards

• Familiarity with common use cases for generative AI in banking (e.g., customer service chatbots, fraud detection, risk assessment)

Soft Skills:

• Strong problem-solving and analytical thinking abilities

• Excellent communication skills to explain complex AI concepts to both technical and non-technical stakeholders

• Ability to work collaboratively in cross-functional teams

• Adaptability to keep up with rapidly evolving AI technologies and methodologies