Senior Associate- Applied AI/MLnew

Join a technology team shaping how a leading global financial institution delivers faster, safer, and more consistent client and employee experiences. You will help build reliable artificial intelligence systems that turn complex, multi-step work into observable, well-controlled workflows. This role offers the opportunity to translate emerging research into practical capabilities, working closely with product partners, engineers, and risk and control stakeholders. You will contribute to solutions designed for quality, safety, and long-term maintainability.

As an Applied Artificial Intelligence and Machine Learning Associate in Private Bank Technology within Asset and Wealth Management, you will design and build agent-based artificial intelligence systems that execute business workflows end-to-end with reliability and appropriate controls. You will develop architectures that manage state, memory, and context; orchestrate tools and integrations; and apply verification and fallback patterns to improve consistency. You will partner with stakeholders to define requirements, success metrics, and controls, and you will help translate advances in large language models (LLMs), retrieval, and reinforcement learning into production-ready solutions. You will contribute to engineering standards that emphasize observability, traceability, and measurable outcomes.

Job responsibilities

• Develop agent-based artificial intelligence solutions across natural language processing, speech analytics, time-series modeling, reinforcement learning, and recommender systems.
• Design agent architectures that combine large language model reasoning with tools, structured data, and application programming interfaces (APIs), including state, memory, and context management.
• Engineer workflow loops that support planning, action, observation, verification, termination, and escalation or fallback to reduce errors and improve reliability.
• Implement control-aware behaviors such as approvals, policy checks, guardrails, and auditable decision paths to support safe deployment.
• Build knowledge-centric grounding layers using knowledge graphs and hybrid retrieval, including retrieval-augmented generation (RAG) and structured sources.
• Author specifications and interface contracts, including schemas, validators, and tool interfaces, to enable testable and maintainable development.
• Create evaluation and regression harnesses aligned to business outcomes, including quality, reliability, latency, cost, and safety.
• Provide technical guidance to peers by sharing patterns, reviews, and documentation that raise engineering rigor and clarity.

Required qualifications, capabilities, and skills

• Advanced degree (master’s or doctoral) in Computer Science, Engineering, Applied Mathematics, Statistics, Operations Research, Data Science, or a related quantitative discipline, or equivalent practical experience.
• Demonstrated experience building agent-based artificial intelligence systems, including state, memory, and context management and tool orchestration for workflow reliability.
• Strong programming skills in Python and experience with machine learning frameworks such as PyTorch or TensorFlow.
• Ability to design experiments and evaluation frameworks with metrics aligned to business outcomes, including quality, reliability, latency, cost, and safety.
• Experience applying software engineering practices that support scalable model development and deployment, including testing, version control, and documentation.
• Strong communication skills to explain technical concepts to both technical and non-technical audiences.

Preferred qualifications, capabilities, and skills

• Experience with search and ranking, reinforcement learning, or meta-learning techniques applied to routing, policies, or self-improvement methods.
• Experience building or applying knowledge graphs, entity resolution, or ontology design in production systems.
• Experience with experimentation practices such as A/B testing and metric-driven product development.
• Experience implementing continuous integration pipelines and unit and integration testing for machine learning-enabled services.