Job Description

We have an exciting and rewarding opportunity for you as a ML Engineer to join the Innovation Lab team to solve exciting business problems in the domain of commercial banking, payments and financial services. Candidates must have a strong curiosity for data and a proven track record of successfully applying rigorous scientific methods with proficiency in ML Engineering. This is a unique opportunity to apply your AI/ML skills and have a direct impact on global business. As a Software Engineer III at JPMorgan Chase, you serve as a seasoned member of an agile team to design and deliver trusted market-leading technology products using AI/ML technologies in a secure, stable, and scalable way. You are responsible for carrying out critical technology solutions across multiple technical areas within various business functions in support of the firm\'s business objectives. You will need to leverage your strong knowledge of ML, NLP, Deep Learning, Knowledge Graphs, and experience in working with massive amounts of data to build systems that reach JP Morgan scale. Job responsibilities Build and train production grade ML models on large-scale datasets to solve various business use cases for Commercial Banking. Use Deep Learning frameworks like CNN, RNN, LSTM and Attention for solvinguse cases requiringsemantic search, named entity resolution, forecasting, anomaly detection among many other techniques. Explore LLM models and evaluate model performance and accuracy. Improve the accuracy of the models by customizing for specific use cases by using tools like Langchain, Few shot learning, Chain of thought and other prompt engineering techniques. Build ML models across Public and Private Cloud environments including container-based Kubernetes environments. Develop end-to-end ML pipelines necessary to transform existing applications and business processes into true AI systems. Build both batch and real-time model prediction pipelines with existing application and front-end integrations. You will collaborate to develop large-scale data modeling experiments, evaluating against strong baselines, and extracting key statistical insights and/or cause and effect relations. Use large scale data processing frameworks such as Spark, AWS EMR for feature engineering and be proficient across various data both structured and un-structured. Required qualifications, capabilities, and skills Advanced Degree in field of Computer Science, Data Science or equivalent discipline Minimum4+ years of working experience as a hands-on Data Scientist/ ML Engineer, with overall 8 to 12 years of industry experience Strong communication skills along with significant experience of managing stakeholder of diverse background Hands on expertise with Python, PySpark, DL frameworks like TensorFlow/PyTorch over GPU is preferred, BERT, SBERT,etc Experience in designing and building highly scalable distributed ML models in production Experience with analytics (ex: SQL, Python, AWS suite, Spark) Experience with machine learning techniques and advanced analytics (e.g. regression, classification, clustering, time series, econometrics, causal inference, mathematical optimization) Preferred qualifications, capabilities, and skills Experience, as a Data Science lead, in driving projects end to end is preferred Experience in large scale Machine Learning system design is preferred Experience working with end-to-end pipelines consisting of Cloud services is preferred Experience with AWS ML ecosystem (i.e. Sagemaker, EMR etc.) is preferred

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