{"id":3060367,"url":"https://alion.io/job/jpmorganchase-lead-applied-ai-machine-learning-engineer","title":"Lead Applied AI & Machine Learning Engineer","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":91,"open_postings":1228,"ghost_share":0.009,"stale_share":0.511,"repost_share":0.058,"time_to_fill_p50_days":9,"computed_at":"2026-10-28T05:59:10Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"lead","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Plano, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":195000,"max_usd":404000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":782},"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"CI/CD","optional":false},{"name":"Context Engineering","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"Function Calling","optional":true},{"name":"RAG","optional":true}],"status":"closed","first_seen_at":"2026-08-13T05:13:07Z","employer_posted_date":"2026-08-13","last_verified_at":"2026-10-28T13:05:57Z","board_verified":false,"closed_at":"2026-10-28T13:05:57Z","days_open":76,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":76},"description":"Build what’s next in enterprise AI-solutions that materially improve how teams make decisions, automate work, and serve internal customers. You will take generative AI from concept to production, help set the standard for semantic consistency across systems, and partner closely with stakeholders to turn complex business needs into measurable outcomes. You will mentor talent and influence technical direction across Corporate Technology and supported Corporate Functions.\nAs an Applied AI and Machine Learning Lead in the Corporate Technology Data Science and AI team, you will Build what’s next in enterprise AI-solutions that materially improve how teams make decisions, automate work, and serve internal customers. In this role, you will take generative AI from concept to production and help set the standard for semantic consistency across systems. You will partner closely with stakeholders to turn complex business needs into measurable outcomes. You will mentor talent and influence technical direction across Corporate Technology and supported Corporate Functions. If you enjoy solving hard problems with real impact, this is the opportunity.\nJob Responsibilities\nBuild generative AI, agentic AI, and large language model solutions in Python from proof of concept through production deployment with measurable outcomes\nDesign context engineering approaches to improve model accuracy, latency, reliability, and end-to-end user experience\nLead enterprise semantic modeling strategy, including ontology standards, governance practices, and lifecycle management\nPartner with domain experts to create scalable ontologies that represent business entities, relationships, rules, and constraints\nDefine semantic integration patterns across data pipelines, application programming interfaces (APIs), data contracts, and experience layers to resolve semantic conflicts\nEstablish and govern a unified semantic layer that enables trusted analytics across business intelligence, machine learning, and transactional systems\nEnable intelligent workflows and AI agents using ontology-driven context, semantic reasoning, and orchestration approaches\nBuild and maintain pipelines and frameworks for model training, evaluation, optimization, monitoring, and machine learning operations\nImplement responsible AI practices, model risk controls, and governance aligned to regulated environments\nMentor engineers and data scientists, raising the bar on engineering rigor, reuse, and continuous improvement across the team\nRequired Qualifications, Capabilities, and Skills\nMaster’s degree in a data science-related discipline and eight years of industry experience, or PhD in a data science-related discipline\nDemonstrated experience developing and deploying machine learning and generative AI solutions using Python\nProven ability to write and maintain production-quality code, including documentation and maintainable design patterns\nExperience building automated testing practices, including unit tests, and implementing continuous integration pipelines\nExperience building and managing data pipelines and processing workflows for analytics and machine learning use cases\nStrong scientific thinking and structured problem-solving skills, including hypothesis-driven analysis and metric definition\nStrong written and verbal communication skills, with the ability to explain complex concepts to technical and non-technical stakeholders\nDemonstrated ownership and attention to detail when operating in ambiguous, complex problem spaces\nAbility to work independently while collaborating effectively across product, engineering, data, and business partners\nPreferred Qualifications, Capabilities, and Skills\nExperience designing or governing semantic models and ontologies, including taxonomy design and lifecycle governance\nExperience implementing retrieval-augmented generation, tool use, and evaluation strategies for large language model applications\nFamiliarity with responsible AI techniques, including bias testing, explainability approaches, and model 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