Machine Learning Engineer (Applied AI ML)

Описание:J.P. Morgan is a global financial services leader that provides strategic advice and products to corporations, governments, wealthy individuals, and institutional investors. Its Commercial & Investment Bank operates across banking, markets, securities services, and payments, providing strategic advice, raising capital, managing risk, and extending liquidity in markets around the world.Задачи:Build scalable Data Science capabilities for multiple business use casesCollaborate with software engineers to design and deploy Machine Learning services integrated with strategic systemsResearch and analyse datasets using statistical and machine learning techniquesCommunicate AI capabilities and results to technical and non-technical audiencesDocument approaches, techniques, and processes to comply with industry regulationCollaborate with cloud and SRE teams and take a leading role in designing and delivering production architecturesAct as an individual contributor; optional management

responsibility may be available depending on experienceТребования:Master’s or PhD in a quantitative discipline, such as Computer Science, Mathematics, or StatisticsSolid understanding of statistics, optimization, and ML theory, with familiarity with deep learning architectures such as transformers, CNNs, and autoencodersSpecialism or well-researched interest in NLPBroad knowledge of MLOps tooling for versioning, reproducibility, and observabilityExperience monitoring, maintaining, and enhancing existing models over an extended periodExtensive experience with PyTorch and related data science Python libraries such as pandasExperience containerising applications or models for deployment using DockerExperience with a major public cloud provider: Azure, AWS, or GCPAbility to communicate technical information and ideas clearly at all levels and build trust with stakeholdersБудет плюсом:designing or implementing DAG-based pipelines using Kubeflow, DVC, or Ray; big data technologies; constructing batch and streaming microservices exposed as REST/gRPC endpoints; container orchestration tools such as Kubernetes or Helm; open-source NLP datasets and benchmarks; implementing distributed, multi-threaded, or scalable applications; a track record of developing and deploying business-critical machine learning models #J-18808-Ljbffr