{"id":1753305,"url":"https://alion.io/job/jpmorganchase-executive-director-machine-learning-engineer-mlops","title":"Executive Director Machine Learning Engineer-MLOps","company":{"id":257,"name":"JPMorganChase","domain":"jpmorganchase.com","url":"https://alion.io/company/jpmorganchase","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Oracle","truth_index":{"grade":"A","score":95,"open_postings":723,"ghost_share":0.01,"stale_share":0.342,"repost_share":0.087,"time_to_fill_p50_days":8,"computed_at":"2026-10-20T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"head","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Palo Alto, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":254000,"max_usd":545000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":86},"experience_years_min":10,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AWQ","optional":false},{"name":"AWS","optional":false},{"name":"CUDA","optional":false},{"name":"CUDA Toolkit","optional":false},{"name":"Fine-tuning","optional":false},{"name":"GRPO","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"Quantization","optional":false},{"name":"Ray","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Transformers","optional":false},{"name":"vLLM","optional":false},{"name":"Amazon ECS","optional":true},{"name":"Kubernetes","optional":true}],"status":"live","first_seen_at":"2026-08-04T19:03:46Z","employer_posted_date":"2026-08-04","last_verified_at":"2026-10-20T10:56:53Z","board_verified":true,"closed_at":null,"days_open":76,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":76},"description":"We are looking for a Senior MLOps engineer to work closely with Data Scientists to build and deploy ML models on a modern MLOps stack.\nAs an Executive Director Machine Learning Engineer on the Recommendation Engine team, you’ll implement fine-tuning and reinforcement learning algorithms on large compute clusters, build and run real-time and batch model serving systems, hyper-parameter tuning at scale, model monitoring, production validation and other activities vital for model development, testing and deployment in a well-managed, controlled environment.\nOur product, Personalization and Insights, builds and supports high throughput, low latency applications which leverage state of the art machine learning architectures, and which are deployed in AWS. These applications power personalized experiences across Chase Consumer & Community Banking channels, to help weave a user experience that includes traditional banking services with other services in the Travel, Merchant Offer Shopping, and Dining spaces.\nJob responsibilities\nBuild, deploy, and maintain robust pipelines for distributed training on GPU-enabled clusters to support scalable machine learning workflows. \nDevelop and manage high-volume real-time and batch inference systems, ensuring optimal performance and reliability. \nImplement quantization techniques and deploy open-weight large language models (LLMs) on modern serving stacks such as vLLM on Ray to maximize efficiency and resource utilization. \nOversee the management and optimization of vector databases to support advanced AI and machine learning applications. \nEstablish and maintain comprehensive monitoring and observability pipelines to ensure system health, performance, and rapid issue resolution. \nCollaborate with cross-functional teams to integrate new technologies and continuously improve existing infrastructure. \nPartner with product, architecture, and other engineering teams to define scalable and performant technical solutions. \nRequired qualifications, capabilities, and skills\nBS in Computer Science or related Engineering field with 10+ years of experience Or MS degree in Computer Science or related Engineering field with 6+ years experience. \nSolid knowledge and extensive experience in Python or in cloud computing and AWS. \nUnderstanding of quantization techniques such as PTQ, AWQ etc. used to quantize LLMs for accelerating inference on specific GPU architectures\nSolid understanding of Transformer models and challenges involved in serving large transformer-based models \nSolid understanding of ML training, especially latest reinforcement learning algorithms such as GRPO and DAPO\nExperience in systems engineering fundamentals: caching, CUDA, autoscaling, high throughput, low latency, x-region resilient applications \nDeep knowledge and passion for data science fundamentals, training and deploying models \nExperience in monitoring and observability tools to monitor model input/output and features stats \nSolid grounding in engineering fundamentals and analytical mindset \nPreferred qualifications, capabilities, and skills\nExperience with recommendation and personalization systems is a plus. \nCUDA experience is a big plus\nSolid fundamentals and experience in containers (docker ecosystem), container orchestration systems [Kubernetes, ECS]\nExperience with Ray, vLLM, RL libraries such as verl/trl \nGood knowledge of Databases\n(i) This position is subject to Section 19 of the Federal Deposit Insurance Act. 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