Generative AI - Senior Associate

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. Job 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 Required qualifications, capabilities, and skills 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 engineeringDemonstrated 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 Preferred qualifications, capabilities, and skills 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 We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive 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. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on‑site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. 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. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants’ and employees’ religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation. JPMorgan Chase & Co. is an Equal Opportunity Employer, including Disability/Veterans. #J-18808-Ljbffr TwinThread