Applied ML and Generative AI Lead - Vice President

Job Responsibilities

• Provide hands-on technical leadership by designing, developing, and deploying ML/LLM/GenAI solutions from concept through production, maintaining ownership for reliability and operability once deployed
• Work closely with product managers, data scientists, ML engineers, and other stakeholders to understand requirements and prioritize use cases
• Mentor and uplift junior engineers through design reviews, code reviews, pairing, and coaching, raising engineering quality and delivery discipline across the team
• Build and institutionalize MLOps capabilities, including automated pipelines for deployment, monitoring, and model lifecycle management, with emphasis on scalability and reliability
• 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-4.1), 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
• Stay informed about the latest trends and advancements in the latest AI/ML/LLM/GenAI research, implement cutting-edge techniques, and leverage external APIs for enhanced functionality

Required Qualifications, Capabilities, And Skills

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field and 7+ years of demonstrated experience in applied AI/ML engineering, with a track record of developing and deploying business critical machine learning models in production
• Demonstrate hands-on engineering leadership: setting technical direction, making architecture decisions, conducting design and code reviews, mentoring junior engineers, and guiding implementation quality across multiple workstreams
• 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 with cloud computing platforms (e.g., AWS, Azure, or Google Cloud Platform), containerization technologies (e.g., Docker and Kubernetes), and microservices design, implementation, and performance optimization
• 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
• Able to identify and address AI/ML/LLM/GenAI challenges, implement optimizations and fine-tune models for optimal performance in NLP applications
• A portfolio showcasing successful applications of generative models in NLP projects, including examples of utilizing OpenAI APIs for prompt engineering

Preferred Qualifications, Capabilities, And Skills

• Familiarity with the financial services industries
• Expertise in designing and implementing pipelines using Retrieval-Augmented Generation (RAG)
• Hands-on knowledge of Chain-of-Thoughts, Tree-of-Thoughts, Graph-of-Thoughts prompting strategies
• Strong collaboration skills to work effectively with cross-functional teams, communicate complex concepts, and contribute to interdisciplinary projects