POSITION OVERVIEW:

We are seeking a highly skilled and motivated Conversational AI Specialist Director with a strong background in prompt engineering and model evaluation to join our team at Morgan Stanley. In this role, you’ll craft enterprise level conversational AI solutions, develop smart automation tools, and refine generative AI models to ensure reliable, quality outputs. This role will liaise with business teams across the Firm, technology, vendors, and centralized AI teams to accelerate the development and adoption of AI-based solutions.

QUALIFICATIONS/SKILLS REQUIRED OR PREFERRED:

• Experience with Conversational AI solutioning / product development / experimentation or Innovation.
• Experience with prompt engineering for using Commercial or Open Source LLMs such as GPT, Claude, Gemini, LLAMA.
• Experience with conversational AI evaluation techniques such as F1, BLEU, ROUGE, and human feedback analysis.
• Strong understanding of artificial intelligence concepts and technologies.
• Excellent communication, interpersonal and leadership skills.
• Proven experience in program or project management
• Ability to manage multiple projects simultaneously in a fast-paced environment.
• Solid analytical and problem-solving abilities with a keen attention to detail

Prompt Engineering: Develop and refine context-aware prompts tailored to different use cases, domains, and user intents with a view of improving the accuracy, relevance and efficiency of AI generated chatbot responses. Ensure these prompt solutions are scalable, sustainable, effectively integrates with technical infrastructure, and successfully meet the specific needs of the business.
Testing and Evaluation: Develop and maintain robust frameworks for testing and evaluating Generative AI responses for coherence, bias, factual accuracy and user satisfaction. Create and manage comprehensive test sets and human evaluations to ensure LLM Model outputs meet high standards.
Troubleshooting and Optimization: Identify and troubleshoot issues and anomalies in the LLM model outputs and provide feedback and recommendations for solution improvement and optimization.
Stakeholder Management: Establish and maintain effective communication channels with internal stakeholders such as tech partners and business product owners. Manage expectations and address any concerns or issues that arise.
Collaboration: Collaborate with cross-functional teams, including internal stakeholders and vendor partners.
Risk Management: Identify potential risk and issues that may impact project delivery and develop mitigation strategies to address them proactively. Monitor project performance and implement corrective actions as necessary to ensure successful outcomes.
Performance Monitoring and Reporting: Track project performance metrics and KPIs to measure progress against objective and identify areas for improvement.