Machine Learning Engineer (Applied AI ML)

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.

• Build scalable Data Science capabilities for multiple business use cases
• Collaborate with software engineers to design and deploy Machine Learning services integrated with strategic systems
• Research and analyse datasets using statistical and machine learning techniques
• Communicate AI capabilities and results to technical and non-technical audiences
• Document approaches, techniques, and processes to comply with industry regulation
• Collaborate with cloud and SRE teams and take a leading role in designing and delivering production architectures
• Act as an individual contributor; optional management responsibility may be available depending on experience

• Master's or PhD in a quantitative discipline, such as Computer Science, Mathematics, or Statistics
• Solid understanding of statistics, optimization, and ML theory, with familiarity with deep learning architectures such as transformers, CNNs, and autoencoders
• Specialism or well-researched interest in NLP
• Broad knowledge of MLOps tooling for versioning, reproducibility, and observability
• Experience monitoring, maintaining, and enhancing existing models over an extended period
• Extensive experience with PyTorch and related data science Python libraries such as pandas
• Experience containerising applications or models for deployment using Docker
• Experience with a major public cloud provider: Azure, AWS, or GCP
• Ability to communicate technical information and ideas clearly at all levels and build trust with stakeholders

Будет 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