As a Gen AI Lead Engineer in the Applied AI/ML team at JPMorgan Corporate Investment Bank, you will be at the forefront of combining cutting-edge AI techniques with the company's 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 the 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 AI-powered products and services and increasing opportunities 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.
Job responsibilities
Build robust data science capabilities which can be scaled across multiple business use cases. Collaborate with the software engineering team to design and deploy machine learning services that can be integrated with strategic systems. Research and analyze 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 regulations. 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
Extensive hands-on experience in an ML engineering role. Extensive experience developing AI-based applications. PhD in a quantitative discipline, e.g., Computer Science, Mathematics, Statistics. Track record of developing and deploying business-critical machine learning models. Broad knowledge of MLOps tooling—for versioning, reproducibility, observability, etc. Experience monitoring, maintaining, and enhancing existing models over an extended time period. Specialism in NLP or computer vision. Solid understanding of the fundamentals of statistics, optimization, and ML theory. Extensive experience with PyTorch, NumPy, and pandas. Familiarity with popular deep learning architectures (e.g., transformers, CNNs, autoencoders). Excellent grasp of computer science fundamentals and development best practices. 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 with big data technologies (e.g., Spark, Hadoop). Hands-on experience in implementing distributed/multi-threaded/scalable applications (including frameworks such as Ray, Horovod, DeepSpeed, etc.). Knowledge of open-source datasets and benchmarks in NLP/computer vision. Experience constructing batch and streaming microservices exposed as REST/gRPC endpoints. Familiarity with GraphQL.