✨ AI Summary
Director-level role responsible for setting the vision and leading the enterprise AI/ML portfolio for agentic AI within a commercial and investment bank. You will define reference architectures and delivery roadmaps for multi-agent systems, implement robust evaluation and safety standards, and drive measurable business outcomes. Key responsibilities include architecting and productionizing autonomous AI agents, designing multi-agent workflows, implementing RAG pipelines and vector search, integrating tools and APIs, and mentoring teams on agent frameworks, LLM usage, safety, and best practices. Required qualifications include an MSc/PhD in a related field, extensive experience in large-scale AI deployments, and proficiency in Python, ML frameworks, and agent technologies; cloud and container technologies are also essential. Preferred qualifications include experience with fine-tuning, prompt optimization, distributed computing, and financial services experience.
Requirements
Director-level role requiring MSc/PhD in Computer Science, Data Science, or related field, with significant proven experience building and deploying AI applications in large-scale production environments. Must have hands-on expertise in Python and ML frameworks, agent frameworks, RAG pipelines, multi-agent system design, cloud and containerization, and strong communication for both technical and non-technical audiences.
Description
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. You will 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, and reliability standards and tracking measurable business outcomes.
Job responsibilities
- 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.
Required 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 couching 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.
Preferred qualifications
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 with AWS AI deployment services (SageMaker, Bedrock) and workflow orchestration.
Demonstrated experience in financial services, particularly investment banking operations.