Job Responsibilities

• Proactively develop understanding of key business problems and processes.
• Execute tasks throughout a model development process including data wrangling/analysis, model training, testing, and selection.
• Implement optimization strategies to fine-tune generative models for specific NLP use cases, ensuring high-quality outputs in summarization and text generation.
• Conduct thorough evaluations of generative models (e.g., GPT-4), iterate on model architectures, and implement improvements to enhance overall performance in NLP applications.
• Implement monitoring mechanisms to track model performance in real-time and ensure model reliability.
• Communicate AI/ML/LLM/GenAI capabilities and results to both technical and non-technical audiences.
• Generate structured and meaningful insights from data analysis and modeling exercises and present them in an appropriate format according to the audience.
• Collaborate with other data scientists and machine learning engineers to deploy machine learning solutions.
• Carry out ad-hoc and periodic analysis as required by the business stakeholder, model risk function, and other groups.

Required Qualifications, Capabilities, and Skills

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
• Minimum 5 years of demonstrated experience in applied AI/ML, with a track record of developing and deploying business-critical machine learning models in production.
• Proficiency in programming languages like Python for model development, experimentation, and integration with OpenAI API.
• Experience with machine learning frameworks, libraries, and APIs, such as TensorFlow, PyTorch, Scikit-learn, and OpenAI API.
• Experience in building AI/ML models on structured and unstructured data along with model explainability and model monitoring.
• Solid understanding of fundamentals of statistics, machine learning (e.g., classification, regression, time series, deep learning, reinforcement learning), and generative model architectures, particularly GANs, VAEs.
• Experience with a broad range of analytical toolkits, such as SQL, Spark, Scikit-Learn, and XGBoost.
• Experience with graph analytics and neural networks (PyTorch).
• Excellent problem-solving, communication (verbal and written), and teamwork skills.

Preferred Qualifications, Capabilities, and Skills

• Expertise in building AI/ML models on structured and unstructured data along with model explainability and model monitoring.
• Expertise in designing and implementing pipelines using Retrieval-Augmented Generation (RAG).
• Familiarity with the financial services industry.