JPMorgan Applied AI ML Lead - Senior Machine Learning Engineer 

United Kingdom, England, London 

650729703

Job responsibilities

• Build robust Data Science capabilities which can be scaled across multiple business use cases
• Collaborate with software engineering team to design and deploy Machine Learning services that can be integrated with strategic systems
• Research and analyse data sets using a variety of statistical and machine learning techniques
• Communicate AI capabilities and results to both technical and non-technical audiences
• Document approaches taken, techniques used and processes followed to comply with industry regulation
• Collaborate closely with cloud and SRE teams while taking a leading role in the design and delivery of the production architectures for our solutions.
• The role is initially that of an individual contributor, though there will be optional opportunity for management responsibility dependent on the candidate’s experience.

Required qualifications, capabilities, and skills

• Hands on experience in an ML engineering role
• PhD in a quantitative discipline, e.g. Computer Science, Mathematics, Statistics
• Track record of developing, deploying business critical machine learning models
• Broad knowledge of MLOps tooling – for versioning, reproducibility, observability etc
• Experience monitoring, maintaining, enhancing existing models over an extended time period
• Specialism in NLP or Computer Vision
• Solid understanding of fundamentals of statistics, optimization and ML theory. Familiarity with popular deep learning architectures (transformers, CNN, autoencoders etc.)
• Extensive experience with pytorch, numpy, pandas
• Knowledge of open source datasets and benchmarks in NLP / Computer Vision
• Hands-on experience in implementing distributed/multi-threaded/scalable applications (incl. frameworks such as Ray, Horovod, DeepSpeed, etc.)
• Able to communicate technical information and ideas at all levels; convey information clearly and create trust with stakeholders.

Preferred qualifications, capabilities, and skills

• Experience designing/ implementing pipelines using DAGs (e.g. Kubeflow, DVC, Ray)
• Experience of big data technologies (e.g. Spark, Hadoop)
• Have constructed batch and streaming microservices exposed as REST/gRPC endpoints
• Familiarity with GraphQL