Generative AI - Senior Associate

JPMorgan Chase & Co.•New York, NY

About The Position

Generative artificial intelligence is transforming how we build products, serve clients, and operate at scale. In the Chief Data and Analytics Office, you will help turn advanced models into dependable, secure, and high-performing production services. You will work with partners across machine learning, cloud engineering, and site reliability engineering to deliver solutions with clear return on investment. If you enjoy hands-on engineering, real-world constraints, and high-impact delivery, this role is for you. As a Senior Associate, Generative AI Engineer in the Chief Data and Analytics Office, you will help design, build, and support production generative artificial intelligence products and reusable backend application programming interfaces used across the firm. You will combine large enterprise datasets with large language and multimodal models to deliver scalable, measurable solutions. You will collaborate closely with machine learning, cloud engineering, and site reliability engineering partners to ensure reliability, performance, and strong operational controls. You will contribute to technical design decisions, delivery planning, and continuous improvement of our platforms and products.

Requirements

PhD in a quantitative discipline such as Computer Science, Mathematics, or Statistics, or equivalent practical experience
3+ years of experience as an individual contributor in machine learning engineering or applied machine learning software engineering
Demonstrated experience delivering production machine learning services in an enterprise environment, including being accountable for service health
Strong fundamentals in statistics, optimization, and machine learning theory with applied depth in natural language processing and/or computer vision
Hands-on experience building distributed, multi-threaded, and scalable systems (for example Ray, Horovod, or DeepSpeed)
Strong software engineering fundamentals, including data structures, algorithms, and software development lifecycle best practices
Experience designing and delivering service-oriented systems and application programming interfaces with scalability and performance requirements
Ability to define success metrics and write clear objectives and key results aligned to business expectations
Strong problem-framing skills to align machine learning solutions to business objectives and constraints
Excellent communication skills with the ability to influence and build trust across technical and non-technical stakeholders

Nice To Haves

Experience designing and implementing pipeline workflows using directed acyclic graph frameworks (for example Kubeflow, DVC, or Ray)
Experience building batch and streaming microservices exposed via gRPC and/or GraphQL
Demonstrable experience with parameter-efficient fine-tuning, quantization, and quantization-aware fine-tuning for large language models
Experience with advanced prompting strategies such as chain-of-thought, tree-of-thought, or graph-of-thought approaches
Experience with multimodal large language model use cases (text plus image, speech, or video)
Experience partnering closely with cloud engineering and site reliability engineering teams on production readiness and operations
Experience measuring and improving model quality using offline evaluation and production monitoring

Responsibilities

Build and operate production generative artificial intelligence services and reusable backend application programming interfaces for firmwide use
Combine enterprise data assets with large language and multimodal models to deliver high-quality user experiences
Design scalable architectures with clear interfaces and separation of concerns to enable broader developer adoption
Implement batch and real-time processing patterns to support high-throughput, low-latency use cases
Collaborate with cloud engineering and site reliability engineering partners to deliver resilient, observable systems
Translate research concepts into production-ready software through experimentation, evaluation, and iterative hardening
Optimize system performance, scalability, and cost across inference, storage, and compute
Define and track measurable outcomes, including objectives and key results aligned to business needs
Ensure responsible artificial intelligence practices, controls, and governance are embedded into delivery and operations
Troubleshoot production issues, drive root-cause analysis, and implement preventative improvements