{"id":2565794,"url":"https://alion.io/job/american-express-senior-agentic-ai-engineer-core-platforms","title":"Senior Agentic AI Engineer - Core Platforms","company":{"id":2209,"name":"American Express","domain":"americanexpress.com","url":"https://alion.io/company/american-express","size_band":"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":50,"ghost_share":0,"stale_share":0.06,"repost_share":0.2,"time_to_fill_p50_days":8,"computed_at":"2026-10-21T05:46:15Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["New York, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":148000,"max_usd":283000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":1268},"experience_years_min":5,"visa_sponsorship":true,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"BERT","optional":false},{"name":"C++","optional":false},{"name":"CI/CD","optional":false},{"name":"Hadoop","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"Java","optional":false},{"name":"LLM","optional":false},{"name":"LLM Evaluation","optional":false},{"name":"Machine Learning","optional":false},{"name":"Multimodal AI","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Spark","optional":false},{"name":"Transformers","optional":false}],"status":"live","first_seen_at":"2026-10-14T19:38:36Z","employer_posted_date":"2026-10-14","last_verified_at":"2026-10-21T15:22:18Z","board_verified":true,"closed_at":null,"days_open":6,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":6},"description":"The Enterprise Technology Services organization partners with every part of the American Express business to power the company’s growth and innovation with trust and efficiency, and drive competitive differentiation with speed. We support the delivery and operations of technology, digital, and data capabilities, platforms, and services globally. Specifically, our team is responsible for the company’s technology engineering, architecture, and infrastructure, providing 24x7 support to ensure an uninterrupted, high-quality experience for customers and colleagues. We also provide product management for core enterprise platforms, and lead technology risk and information security, enterprise data governance and platforms, digital product and design, and enterprise AI platforms on behalf of the company.\nThe Evaluation Engineer designs, develops, and maintains evaluation frameworks for AI and machine learning solutions, including LLM, RAG, agentic, and multimodal applications. This role translates business, product, risk, and governance requirements into measurable success criteria; builds automated and human-in-the-loop evaluation pipelines; and partners with cross-functional teams to ensure AI systems meet enterprise standards for quality, safety, reliability, compliance, latency, and cost.\n Mentors junior AI Engineers and cross-functional team of colleagues, fostering a culture of excellence and innovation\nIntegrates AI models into production systems in collaboration with cross-functional teams, ensuring effective integration into existing production systems\nMaintains strict adherence to model governance, security, and compliance standards\nProvides guidance and support to junior engineers, fostering professional growth and development within the team\nDesigns and implements AI/machine learning frameworks for data processing, focusing on prompt engineering and generative AI technologies\nCollaborates with engineers to deliver scalable AI solutions by integrating models that meet both technical and business requirements\nTranslates business requirements into AI-driven technical solutions, ensuring that business needs are converted into actionable models and algorithms\nDevelops reusable Big Data pipelines and AI-based systems, ensuring efficiency and consistency in data processing and model deployment\nExperiments with and deploys new natural language processing models to classify and analyze business-related text data, conducting research to improve text classification and analysis\nCreates end-to-end machine learning operations pipelines for scalable model deployment, facilitating seamless deployment, monitoring, and maintenance of AI models\nCollaborates and co-creates effectively with teams in product and the business to align technology initiatives with business objectives\nDesigns and operates scalable production AI systems, integrating ML models, LLMs, and intelligent services across multiple components\nLeads development of AI capabilities such as robust inference services, retrieval pipelines (e.g., RAG), orchestration layers, and evaluation frameworks\nEnsures AI systems adhere to enterprise standards for reliability, safety, explainability, observability, and compliance, mentoring others on best practices\nDefines evaluation strategies, success criteria, benchmarks, and scorecards for LLM, RAG, agentic, and multimodal AI solutions across quality, safety, reliability, latency, and cost.\nBuilds automated evaluation pipelines using curated datasets, synthetic test generation, human review, model-based grading, and deterministic checks; validates evaluators for consistency and bias.\nEstablishes regression testing, release gates, and continuous evaluation to detect quality degradation, prompt or model drift, unsafe behavior, and changes introduced by models, data, tools, or orchestration.\nPartners with product, engineering, risk, and governance teams to convert use-case requirements and policy expectations into measurable acceptance criteria and traceable evidence.\nPerforms failure analysis and communicates actionable findings, tradeoffs, and remediation priorities to engineering and business stakeholders. \n Education and Knowledge\n5+ years' Experience in software development, including hands on experience in machine learning, AI Technologies, and Agentic solutions\nBachelor's degree in computer science, Machine Learning, Data Science, and/or comparable experience; advanced degree preferred\nKnowledge of Python and data processing technologies such as Big Data (Hadoop, Spark)\nKnowledge of natural language processing techniques like fuzzy wuzzy and models such as BERT, Transformer models, and their real-world applications\nAdvanced knowledge of machine learning techniques and performance optimization\nStrong understanding of AI system architectures including LLM ecosystems, retrieval systems, orchestration frameworks, and evaluation methodologies\nWork Experience\nExperience in software development with a focus on machine learning, generative AI, Big Data, and prompt engineering\nExperience in working with large language models (LLMs), especially GPT-3.5/4 or similar AI models\nExperience in languages such as Python, R, Java, or C++\nExperience in deploying AI models and pipelines using machine leanring operations tools\nExperience in machine learning algorithms and experience applying them to real-world problems\nExperience in continuous integration/continuous deployment pipelines and containerized deployments\nExperience in building and maintaining data pipelines and extract, transform, and load processes\nExperience delivering end-to-end AI systems in production, including design and operational ownership\nExperience with distributed systems design, scalable APIs, and cloud-native AI infrastructure, including monitoring and optimization\nAdvanced expertise in a technical domain and moderate expertise in a new, different technical domain\nExperience building evaluation harnesses and automated test pipelines for LLM, RAG, or agentic applications using production-representative datasets.\nExperience designing human-in-the-loop and model-based evaluation workflows, including calibration, error analysis, and validation of evaluation quality.\nExperience integrating evaluation results into CI/CD, release readiness, production monitoring, and governance evidence. \nEmployment eligibility to work with American Express in the United States is required as the company will not pursue visa sponsorship for these positions.","description_format":"text","description_chars":6504,"description_truncated":false,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":true},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Commercial & Retail Banks","Cards & Card Issuing","Payment Processing & Gateways"],"lifecycle":[{"event":"open","at":"2026-10-14T21:55:44Z"}],"visa":[{"country":"US","licensed_sponsor":true,"evidence":"H-1B filings in 12 months: 101 · green card filings: 30","filings_12m":101,"filings_prev_12m":132,"green_card_filings_12m":30,"median_offered_wage_usd":139485,"route":null,"cap_exempt":false,"checked_at":"2026-10-15T11:24:14+00:00","sources":["US Department of Labor: LCA disclosure data (H-1B, H-1B1, E-3)","US Department of Labor: PERM disclosure data (green cards)"],"filings_for_role_12m":45}],"liveness":{"score":62,"band":"ok","label":"Likely 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