Vice President, AI Agents Research & Engineering

Archer; New York (NY); On-site; USD 180,000 - 270,000; Full time

Benefits offered by this job

Health care coverage

On-site health centers

Retirement plan

Tuition reimbursement

Mental health support

Job summary

Archer, in collaboration with JPMorgan Chase, seeks an AI Agents Applied Research/Engineering Lead to shape how millions of customers interact with financial tasks via multi-step conversations. You will own end-to-end lifecycle of LLM-based agents from research to production deployment, balancing latency, accuracy and compliance in a highly regulated domain.

The role demands a Ph.D. with production AI experience or an MS with several years, plus strong Python, PyTorch/TensorFlow, and ML tooling

Qualifications

• Ph.D. with 1 year of production AI deployment or MS with 3 years of production AI deployment experience.
• Applied GenAI with LLMs including fine-tuning, prompting, and RAG.
• Experience scaling LLM systems with caching, batching, governance, and evaluation.

Responsibilities

• Lead research and deployment of agentic AI systems with multi-step workflows.
• Fine-tune and optimize LLMs for production constraints including latency and cost.
• Apply reinforcement learning and preference optimization to improve dialogue policies.
• Scale LLM systems with caching, batching, and governance.
• Ensure privacy, safety, and security controls including PCI compliance and auditability.
• Define experiments with rigorous baselines and meaningful metrics.
• Track success metrics like task completion, accuracy, latency, and customer satisfaction.

Skills

LLM deployment

PEFT

Reinforcement learning

Tool calling

Multi-agent orchestration

Python

PyTorch

TensorFlow

Hugging Face

scikit-learn

Education

Ph.D.

MS in relevant field

Tools

PyTorch

TensorFlow

Hugging Face

scikit-learn

Job description

Archer, in collaboration with JPMorgan Chase, seeks an AI Agents Applied Research/Engineering Lead to shape how millions of customers interact with financial tasks via multi-step conversations. You will own end-to-end lifecycle of LLM-based agents from research to production deployment, balancing latency, accuracy and compliance in a highly regulated domain.

The role demands a Ph.D. with production AI experience or an MS with several years, plus strong Python, PyTorch/TensorFlow, and ML tooling