{"id":3181838,"url":"https://alion.io/job/campbell-global-lead-machine-learning-engineer-mlops-vice-president","title":"Lead Machine Learning Engineer-MLOps Vice President","company":{"id":2649833,"name":"Campbell Global","domain":"campbellglobal.com","url":"https://alion.io/company/campbellglobal","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Oracle","truth_index":{"grade":"A","score":100,"open_postings":77,"ghost_share":0.013,"stale_share":0.013,"repost_share":0.078,"time_to_fill_p50_days":9,"computed_at":"2026-10-25T05:49:26Z"}},"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":["Jersey City, United States","Palo Alto, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":142000,"max_usd":306000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":86},"experience_years_min":6,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AWS","optional":false},{"name":"DuckDB","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"Quantization","optional":false},{"name":"Ray","optional":false},{"name":"Spark","optional":false},{"name":"Amazon ECS","optional":true},{"name":"Kubeflow","optional":true},{"name":"Kubernetes","optional":true}],"status":"live","first_seen_at":"2026-10-23T01:19:49Z","employer_posted_date":"2026-10-23","last_verified_at":"2026-10-25T10:53:39Z","board_verified":true,"closed_at":null,"days_open":2,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":2},"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 Lead Machine Learning Engineer on the Recommendation Engine team, you’ll build and maintain pipelines for distributed model training on large compute clusters, batch/real-time model serving, hyperparameter 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.\n\nDevelop and manage pipelines for high-throughput, real-time inference as well as batch inference, ensuring optimal performance and reliability.\n\nImplement quantization techniques and deploy large language models (LLMs) to maximize efficiency and resource utilization.\n\nOversee the management and optimization of vector databases to support advanced AI and machine learning applications.\n\nEstablish and maintain comprehensive monitoring and observability pipelines to ensure system health, performance, and rapid issue resolution.\n\nCollaborate with cross-functional 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