Senior GenAI Architect

Job Description:
About the Role
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).

The Role
Responsibilities:
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.
Develop and deliver cutting-edge AI/ML solutions, incorporating genetic AI techniques, innovative design principles, and scalable deployment strategies.
Gain a good understanding of traditional AI/ML approaches and leverage this knowledge to create robust, hybrid solutions.
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, including tracing frameworks, LLM observability, and other innovative areas, recommending adoption of best practices and solutions.

Requirements:
Experience in leading AI/ML architecture and strategy in enterprise environments.
Strong expertise in designing and deploying large-scale AI/ML solutions, including LLMs, RAG frameworks, and genetic AI techniques.
Experience with AI/ML tools and frameworks such as TensorFlow, PyTorch, Hugging Face, LangChain, LangGraph, or similar.
Agentic AI experience: design, develop, and deliver tracing frameworks and LLM observability solutions.
Deep understanding of data workflows, feature engineering, model training, evaluation, and deployment.
Good understanding of traditional AI/ML concepts, alongside expertise in generative AI and related frameworks.
Hands-on experience with AI/ML model observability, tracing frameworks, and monitoring solutions.
Knowledge of cloud platforms (AWS, Azure, GCP) 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.

Preferred, but not required:
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.
Familiarity with project management and Agile practices.