AI Agents Applied Engineer Senior Associate

We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. Youll have the opportunity to publish at top-tier venues like NeurIPS ICML and ACLand see that research deployed to a user base of over 80 million customers.

As an AI Agents Applied Research/Engineering Senior Associate in our The Digital Team you will work with the team to shape how millions of customers discover decide and actturning multi-step financial tasks into simple conversations. Youll lead the end-to-end lifecycle of LLM-based agents: defining research directions in areas like multi-step planning tool use and safety; building production systems that perform under real-world latency accuracy and compliance constraints; and partnering with Product Engineering Design and Risk teams to bring those systems to market. The problems here are genuinely unusualbuilding AI that must be not just accurate but auditable explainable and safe in a highly regulated high-stakes domain. Transform how millions of customers manage their money make decisions and get more from their financial relationships through a human-centered approach that blends cutting-edge AI with clear trustworthy experiences.

Job Responsibilities

• Perform research and deployment of agentic AI systems with multi-step workflows tool calling and multi-agent orchestration.

• Fine-tune and optimize LLMs using parameter-efficient fine-tuning (PEFT) distillation and quantization to meet production constraints such as latency memory and cost.

• Apply reinforcement learning and preference optimization to improve personalization and dialogue policies.

• Scale LLM systems through caching batching prompt governance and evaluation frameworks.

• Implement privacy safety and security controls including PCI compliance jailbreak resistance and auditability.

• Design rigorous experiments with strong baselines and meaningful metrics.

• Define and track success metrics for agent performance including task completion rate accuracy latency and customer satisfaction.

Required Qualifications Capabilities and Skills

• BS with 3 years or M.S. with 2 years building and deploying AI systems in production

• Applied GenAI experience with LLMs including fine-tuning prompt engineering and RAG.

• Experience scaling LLM systems with caching batching governance and evaluation.

• Strong foundation in ML deep learning statistical modeling and experimental design.

• Experience in Information Retrieval (indexing ranking retrieval) and/or recommendation systems.

• Proficiency in Python and ML frameworks (PyTorch/TensorFlow Hugging Face scikit-learn)

• Demonstrated ability to set a technical research agenda and drive it from concept through production deployment.

Preferred Qualifications Capabilities and Skills

• 2 years developing conversational AI systems virtual assistants or LLM-based systems in production.

• Experience with multi-agent orchestration supervisor agents and specialized toolkits.

• Experience with reinforcement learning bandit algorithms and preference-based optimization (DPO IPO) with practical exposure to data collection labeling and evaluation pipelines.

• MLOps/LLMOps experience with CI/CD monitoring versioning A/B testing and rollbacks.

• Track record of data-driven product development and experimentation.

• Publications in top-tier AI/ML venues and/or open-source contributions