Applied AI ML Sr Associate - Operations Tech

JPMorgan Chase & Co. • Wilmington, DE

13h

About The Position

Join a team where you can turn cutting-edge AI into practical outcomes that improve how knowledge is created, found, and used. You will help build and scale applied AI solutions that automate workflows, enhance decision-making, and strengthen operational efficiency across our knowledge management and operations ecosystem. As an Applied AI ML Associate Senior at JPMorganChase within Consumer & Community Banking Knowledge Management and Operations, you will design and deliver production-grade AI solutions that apply Large Language Models, Natural Language Processing, and AI agents to real business workflows. You will collaborate with partners across product and operations to translate needs into measurable outcomes, while building scalable services that are reliable, secure, and well-governed. Your work will directly improve knowledge discovery, reduce manual effort, and accelerate how teams serve customers and employees.

Requirements

• Formal training or certification on "Applied AI/ML" concepts and 3+ years applied experience
• 3+ years of applied experience building and delivering production-quality software in Python, including testing and API development.
• 3+ years of applied experience developing and deploying machine learning models, including training, evaluation, and inference.
• Applied experience delivering NLP or LLM-enabled solutions (for example: embeddings, retrieval, and prompt engineering) in production environments.
• Experience building and operating cloud-based solutions on AWS, including deployment automation and security fundamentals.
• Experience designing model evaluation and monitoring approaches, including quality metrics and performance tracking.
• Strong problem-solving skills with the ability to break down ambiguous challenges into hypotheses, experiments, and deliverables.
• Strong communication skills and experience partnering cross-functionally to deliver outcomes in complex environments.

Nice To Haves

• Experience implementing retrieval-augmented generation systems, including document chunking strategies, vector search, and relevance tuning.
• Experience building AI agent workflows that integrate LLMs with tools and APIs, with robust guardrails and fallback behaviors.
• Familiarity with MLOps practices on AWS (for example: CI/CD for ML, infrastructure as code, and automated model deployments).
• Experience with large-scale data processing and orchestration patterns (batch and streaming) for ML pipelines.
• Experience with advanced evaluation methods for LLM applications (for example: regression suites, synthetic test generation, and human review workflows).
• Prior experience delivering AI-enabled knowledge management, operations automation, or enterprise

Responsibilities

• Design, build, and deploy Python-based machine learning and natural language processing solutions that improve knowledge discovery and operational efficiency.
• Implement and productionize Large Language Model solutions, including retrieval-augmented generation and AI agent workflows, to automate high-volume, repeatable processes.
• Develop and maintain data pipelines that curate, transform, and validate datasets used for model training and inference.
• Build and operate inference services and APIs on AWS, optimizing for latency, reliability, and cost.
• Create evaluation and testing frameworks for LLM and NLP quality, including automated scoring and human review workflows, and use results to drive continuous improvement.
• Partner with product, operations, and subject matter experts to translate business problems into measurable ML outcomes, success metrics, and deployment-ready deliverables.
• Establish observability for AI services, including monitoring, alerting, logging, and drift detection, and lead production issue triage and remediation.
• Apply responsible AI and data governance practices in solution design, including privacy and security controls, to support safe and scalable adoption.

Benefits

• competitive total rewards package including base salary determined based on the role, experience, skill set and location.
• commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions.
• comprehensive health care coverage
• on-site health and wellness centers
• a retirement savings plan
• backup childcare
• tuition reimbursement
• mental health support
• financial coaching

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed
• Number of Employees: 5,001-10,000 employees