Hybrid: Gen AI Architect

The Gen AI Architect will leverage advanced Generative AI models and Azure OpenAI services to develop innovative solutions for investment banking processes. The ideal candidate will have a strong background in investment banking, hands-on experience with Microsoft Azure OpenAI, and expertise in Retrieval-Augmented Generation (RAG).

• Define and drive the AI/ML architecture and roadmap, including both traditional machine learning and Generative AI (GenAI) use cases.
• Design comprehensive end-to-end AI solutions covering data ingestion, feature engineering, model training, inference pipelines, and monitoring frameworks.
• Lead the integration of Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) frameworks, utilizing tools such as LangChain, LangGraph, or similar.
• Collaborate with business stakeholders to translate requirements into scalable AI-driven technical solutions.
• Evaluate and select appropriate AI/ML tools, cloud services, frameworks, and libraries based on use case needs and industry best practices.
• Ensure models adhere to governance, security, explainability, and regulatory compliance, embedding ethical AI principles into system design.
• Guide engineering teams in the implementation of AI components, emphasizing scalability, reliability, and performance optimization.
• Partner with DevOps teams to establish CI/CD pipelines for AI, including model versioning, deployment automation, and ongoing A/B testing.
• Keep abreast of the latest industry research, breakthroughs, and emerging trends in AI, recommending adoption of best practices and innovative solutions.

• Proven experience 10+ years, excel in leading AI/ML architecture and strategy in enterprise environments.
• Strong expertise in designing and deploying large-scale AI/ML solutions, including LLMs and RAG frameworks.
• Experience with AI/ML tools and frameworks such as TensorFlow, PyTorch, Hugging Face, LangChain, LangGraph, or similar.
• Deep understanding of data workflows, feature engineering, model training, evaluation, and deployment.
• Knowledge of cloud platforms (AWS, Azure, Google Cloud Platform) and services tailored for AI deployment.
• Familiarity with model governance, security, explainability, and ethical AI standards.
• Experience in developing CI/CD pipelines for AI/ML, including model versioning, monitoring, and performance tuning.
• Strong problem-solving, communication, and stakeholder management skills.

• Advanced degree (Ph.D., Master’s) in Computer Science, Data Science, AI, or related fields.
• Publications or practical contributions to AI research and open-source projects.
• Experience working in regulated industries or environments requiring compliance and governance.
• Familiar with project management and Agile practices