AI/ML Director

Overview

As a Director of AI/ML within the Commercial and Investment Bank, you will set the vision and lead the enterprise portfolio for agentic AI that automates and optimizes complex business workflows at scale.

Responsibilities

• Guide cross-functional teams and strategic partners to define reference architectures and delivery roadmaps for multi-agent systems—leveraging LLMs, retrieval-augmented generation, and modern agent frameworks—while instituting governance, safety, reliability standards and tracking measurable business outcomes.
• Architect, develop, and productionize autonomous and assistive AI agents to streamline and enhance operations.
• Design multi-agent systems, including role definition, tool integration, planning, memory, and workflow orchestration using LangChain, CrewAI, AutoGen, ADK and LangGraph.
• Implement Retrieval-Augmented Generation (RAG) pipelines and semantic search using vector databases such as Pinecone and Chroma, including indexing, retrieval policies, and evaluation.
• Build and integrate agent tools (MCP) and APIs to connect agents with external services, databases, and internal systems, ensuring robust output parsing, error handling, and retries.
• Design and implement robust evaluation frameworks to systematically assess and measure the performance of AI agents across key operational metrics.
• Practice advanced prompt and context engineering (e.g., Chain-of-Thought, ReAct, function calling/tool-use prompts); implement output validation and guardrails to reduce hallucinations.
• Deploy scalable AI services to cloud infrastructure, ensuring monitoring and observability for agent performance.
• Design microservices-based architectures and orchestrate multi-step workflows; instrument agents for tracing, metrics, and feedback loops to continuously improve reliability and utility.
• Partner with stakeholders to define requirements, design intuitive human-AI interfaces (voice, chat), and deliver measurable business impact.
• Analyze data to inform agent capabilities, optimize retrieval, and drive data-driven decision-making and performance evaluations.
• Mentor and guide team members on agent frameworks, LLM usage, safety, and best practices.

Qualifications

• MSc/PhD in Computer Science, Data Science, Machine Learning, or related field.
• Significant proven experience building and deploying AI applications in large-scale production environments.
• Experience managing data science teams and coaching team members.
• Proficiency in Python and ML frameworks (PyTorch, TensorFlow, scikit-learn).
• Hands-on experience with agentic frameworks (LangChain, CrewAI, AutoGen, LangGraph, ADK).
• Experience with generative models (transformers, GANs/VAEs; diffusion models a plus).
• Strong understanding of data preprocessing, feature engineering, and evaluation techniques.
• Familiarity with cloud platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes).
• Strong communication skills for both technical and non-technical audiences.
• Experience fine-tuning small language models (SLMs) with LoRA, QLoRA, DoRA; quantization and distillation a plus.
• Familiarity with prompt optimization frameworks (AutoPrompt, DSPy) and building evaluation suites.
• Experience with distributed computing, data sharding, and performance optimization.
• Hands-on experience with AWS AI deployment services (SageMaker, Bedrock) and workflow orchestration.
• Demonstrated experience in financial services, particularly investment banking operations.

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