{"id":2226140,"url":"https://alion.io/job/the-infatuation-applied-aiml-lead-python-agentic-ai","title":"Applied AIML Lead-Python & Agentic AI","company":{"id":1048574,"name":"The Infatuation","domain":"theinfatuation.com","url":"https://alion.io/company/the-infatuation","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":159,"ghost_share":0.019,"stale_share":0,"repost_share":0.277,"time_to_fill_p50_days":8,"computed_at":"2026-10-10T05:45:15Z"}},"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":["Glasgow, United Kingdom"],"countries":["GB"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":90000,"max_usd":188000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":17},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Docker","optional":false},{"name":"GCP","optional":false},{"name":"GPT-5","optional":false},{"name":"Kubernetes","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"NLP","optional":false},{"name":"OpenAI","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Scikit-learn","optional":false},{"name":"TensorFlow","optional":false},{"name":"Time Series Forecasting","optional":false},{"name":"RAG","optional":true}],"status":"live","first_seen_at":"2026-10-09T17:31:44Z","employer_posted_date":"2026-10-09","last_verified_at":"2026-10-10T22:01:04Z","board_verified":true,"closed_at":null,"days_open":1,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":1},"description":"The Applied Artificial Intelligence and Machine Learning (Applied AI/ML) team within Infrastructure Platforms is transforming how the firm delivers strategic infrastructure platforms-based solutions-both by applying AI/ML within engineering workflows and by building scalable AI hosting platforms and capabilities for enterprise use.\nAs a Machine Learning Lead Software Engineer at JPMorgan Chase within the Governance & Controls Technology AI/ML team, you serve as a seasoned member of an agile team to design and deliver trusted market-leading technology products in a secure, stable, and scalable way. You are responsible for carrying out critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.\nJob responsibilities\nWork closely with product managers, data scientists, ML engineers, and other stakeholders to understand requirements and prioritize use cases. \nDesign, develop, and deploy state-of-the-art AI/ML/LLM/GenAI solutions to meet business objectives. \nManage, mentor, and guide a team of ML and MLOps engineers. \nDevelop and maintain automated pipelines for model deployment, ensuring scalability, reliability, and efficiency. \nImplement optimization strategies to fine-tune generative models for specific NLP use cases, ensuring high-quality outputs in summarization and text generation. \nConduct thorough evaluations of generative models (e.g., GPT-5.5), iterate on model architectures, and implement improvements to enhance overall performance in NLP applications. \nImplement monitoring 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Platform), containerization technologies (e.g., Docker and Kubernetes), and microservices design, implementation, and performance optimization. \nSolid understanding of fundamentals of statistics, machine learning (e.g., classification, regression, time series, deep learning, reinforcement learning), and generative model architectures, particularly GANs, VAEs. \nAbility to identify and address AI/ML/LLM/GenAI challenges, implement optimizations and fine-tune models for optimal performance in NLP applications. \nStrong collaboration skills to work effectively with cross-functional teams, communicate complex concepts, and contribute to interdisciplinary projects. \nA portfolio showcasing successful applications of generative models in NLP projects, including examples of utilizing OpenAI APIs for prompt engineering.\nPreferred qualifications, capabilities, and skills\n\nFamiliarity with the financial services industries. \nExpertise in designing and implementing pipelines using 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