Applied AI ML Lead - DocAI
Applied AI ML Lead - DocAI
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Job Description
Take a technical leadership position within JPMorgans Commercial & Investment Bank where youll harness cutting-edge AI techniques to revolutionize business decisions and automate processes.
As an Applied AI / ML Lead Vice President - Machine Learning Engineer in the Applied AI ML team at JPMorgan Commercial & Investment Bank you will be at the forefront of combining cutting-edge AI techniques with the companys unique data assets to optimize business decisions and automate processes. You will have the opportunity to advance the state-of-the-art in AI as applied to financial services leveraging the latest research from fields of Natural Language Processing Computer Vision and statistical machine learning. You will be instrumental in building products that automate processes help experts prioritize their time and make better decisions. We have a growing portfolio of AIpowered products and services and increasing opportunity for re-use of foundational components through careful design of libraries and services to be leveraged across the team. This role offers a unique blend of scientific research and software engineering requiring a deep understanding of both mindsets. The role is initially that of an individual contributor though there will be optional opportunity for management responsibility dependent on the candidates experience.
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
Required qualifications capabilities and skills
- Hands on experience in an ML engineering role
- Masters degree or 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
- 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
Employment Type
Full-Time
