We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.

As a Lead Software Engineer at JPMorgan Chase within the Consumer and Community Banking, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.

Job responsibilities

 

Design and implement end-to-end machine learning solutions for the production environment to solve complex problems related to personalized financial services in retail and digital banking. Work closely with other Machine Learning practitioners and cross-functional teams to translate business requirements into technical solutions and drive innovation in our banking products and services. Collaborate with Machine Learning engineers, product managers, key business stakeholders, engineering, and platform partners to deploy projects that deliver cutting-edge machine learning-driven digital solutions. Write code to create several machine learning experimentation pipelines. Design and implement feature engineering pipelines and push them to feature stores. Collaborate with data engineers and product analysts to preprocess and analyze large datasets from multiple sources. Execute experiments and validations at scale, and review results with Lead and Products. Create model serving pipelines that meet consumption SLAs. Write production-grade code for both training and inference functions. Collaborate with MLOps engineers in developing and testing the training and inference applications under the production architecture blueprint, often in integration with upstream and downstream applications. Collaborate with MLOps engineers to register the models' artifacts, maintain code repositories, and prepare for CI/CD execution and post-production monitoring setups. Drive end-to-end system architecture in collaboration with ML, MLOps, and Architecture leads. Communicate and collaborate with Platform and Engineering partners to bring in the latest advancements to improve the scale, consistency, reliability, and trustworthiness of the ML solutions. Mentor Junior Machine Learning associates in delivering successful projects and building successful careers in the firm. Participate and contribute back to firm-wide Machine Learning communities through patenting, publications, and speaking engagements

 

Required qualifications, capabilities, and skills

 

Formal training or certification on software engineering concepts and 5+ years applied experience BS, MS or PhD degree in Computer Science, Statistics, Mathematics or Machine learning related field. Expert proficiency in implementing ML models at least one of the following areas: Natural Language Processing, Knowledge Graph, Computer Vision, Speech Recognition, Reinforcement Learning, Ranking and Recommendation, or Time Series Analysis. Foundational knowledge in Data structures, Algorithms, Machine Learning, Data Mining, Information Retrieval, Statistics. Demonstrated expertise in machine learning frameworks: Tensorflow, Pytorch, pyG, Keras, MXNet, Scikit-Learn. Expert programming knowledge of python, spark; Expert coding knowledge on vector operations using numpy, scipy;  Coding knowledge on distributed computation using Multithreading, Multi GPUs, Dask, Ray, Polars etc.  Strong analytical and critical thinking skills for problem solving. Excellent written and oral communication along with demonstrated teamwork skills. Demonstrated ability to clearly communicate complex technical concepts to both technical and non-technical audiences. Experience in working with interdisciplinary teams and collaborating with other researchers, engineers, and stakeholders. A strong desire to stay updated with the latest advancements in the field and continuously improve one's skills Preferred qualifications, capabilities, and skills Experience with distributed data/feature engineering using popular cloud services like AWS EMR Experience with large scale training, validation and testing experiments Experience with cloud Machine Learning services in AWS i.e. Sagemaker Experience with Container technology like Docker, ECS etc. Experience with Kubernetes based platform for Training or Inferencing