Quantitative Analytics Manager

Foundation Model Training

Experience with pre-training, supervised fine-tuning (SFT) and post-training methodologies including RLHF, RLAIF, PPO, DPO, and GRPO

Training and deploying models in cloud environments, including GCP

Agentic AI

Multi-agent architectures and orchestration frameworks like LangChain, LangGraph, LangChain, LlamaIndex, Google's ADK, CrewAI

Retrieval-Augmented Generation (RAG) applications and intelligent agent deployment

AI Infrastructure & Optimization

Distributed GPU training

Efficient model tuning approaches such as LoRA and PEFT

You will be hands-on working and delivering high-impact projects involving foundation models, large language models, multi-agent workflows, retrieval-augmented generation, human-in-the-loop AI, model evaluation, automation, and responsible AI deployment at enterprise scale. In addition, you will also lead a team working on building Agentic and Gen AI applications.

Design, pre-train, fine-tune, and evaluate transformer-based, foundation, and small language models (SLMs).

Develop and deploy AI and machine learning solutions across generative AI, agentic systems, and traditional machine learning applications

Design and build LLM-powered agents and multi-agent systems capable of planning, reasoning, task orchestration, and human-in-the-loop collaboration

Monitor production models and AI systems, evaluating performance, stability, and model drift through testing and analytics frameworks

Collaborate with cross-functional teams, technical experts, and business leaders across the organization

Gain exposure to enterprise-scale AI development, governance, and risk management practices

Apply AI, machine learning, and generative AI techniques to solve complex business problems.

Build and optimize scalable model training and deployment pipelines.

Leverage distributed computing and advanced training techniques to improve model performance and efficiency.

Enhance model performance through distillation, quantization, and pruning.

Optimize inference speed, latency, throughout, and cost for production AI systems.

*Job posting may come down early due to volume of applicants.

Wells Fargo is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other legally protected characteristic.

Employees support our focus on building strong customer relationships balanced with a strong risk mitigating and compliance-driven culture which firmly establishes those disciplines as critical to the success of our customers and company. They are accountable for execution of all applicable risk programs (Credit, Market, Financial Crimes, Operational, Regulatory Compliance), which includes effectively following and adhering to applicable Wells Fargo policies and procedures, appropriately fulfilling risk and compliance obligations, timely and effective escalation and remediation of issues, and making sound risk decisions. There is emphasis on proactive monitoring, governance, risk identification and escalation, as well as making sound risk decisions commensurate with the business unit’s risk appetite and all risk and compliance program requirements.

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