We have an exciting and rewarding opportunity for you to take your software engineering career to the next level.

As a Applied AI/ML Lead - Data Scientist at JPMorgan Chase within the Commercial Bank's Tech Platform and Shared Services team, you serve as a seasoned member of an agile team to design and deliver trusted market-leading technology products 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.

Commercial Banking (CB) serves more than 30,000 clients, including corporations, municipalities, financial institutions, and not-for-profit entities with annual revenues generally ranging from $20 million to $2 billion. Our Commercial Bankers serve these clients by operating in 14 of the 15 top U.S. major markets. Our professionals' industry knowledge and experience combined with our dedicated service model, comprehensive solutions, and local expertise to make us the #1 commercial bank in our retail branch footprint. 

Job responsibilities

Deploy production grade ML models on large-scale datasets to solve various business use cases for Commercial Banking. 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. Implement Retrieval-Augmented Generation (RAG) methods to enhance the LLM's ability to retrieve and generate accurate answers from large datasets. Develop end-to-end ML pipelines necessary to transform existing applications and business processes into true AI systems. Collaborate with cross-functional teams, including data scientists, software engineers, and product managers, to define requirements and deliver high-quality solutions.  Utilize Deep Learning frameworks like CNN, RNN, LSTM and Attention for solving use cases requiring semantic search, named entity resolution, forecasting, anomaly detection among many other techniques. Collaborate to develop large-scale data modeling experiments, evaluating against strong baselines, and extracting key statistical insights and/or cause and effect relations. Utilize Prompt Engineering techniques to fine-tune and optimize LLMs for specific use cases and improve response accuracy and relevance.

Required qualifications, capabilities, and skills 

Formal training or certification on software engineering concepts and 10+ years applied experience Fluent in programming language e.g. Python Experience working with end-to-end ML pipeline orchestrators using frameworks like MLFlow, KubeFlow, TensorFlow, Apache Airflow, Step Function Experience with any of the AI/machine learning frameworks, statistical packages, and libraries: TensorFlow, Amazon Machine Learning, Apache Spark, PyTorch, Scikit-learn etc Experience with Enterprise Cloud infrastructure (AWS, Azure, GCP) in a mission critical environment Hands-on experience with cloud-based technologies and tools especially in deployment, monitoring and operations, such as Data Dog, Prometheus, Splunk, Apica, Dynatrace Elasticsearch, Grafana. Knowledge of CI/CD is mandatory. Hands on experience on terraform for infrastructure development is mandatory. Hands on experience on Jenkins or similar tools is mandatory. Hands on experience with some of the common cloud databases like RedShift, Postgres, Elasticsearch, Neo4j/Neptune are expected.  Candidates must have a strong engineering, ML knowledge , cloud automation and DevOps background and passionate about applying engineering methodology and best practice to machine learning development life cycle.

 Preferred qualifications, capabilities, and skills 

Experience on AWS services like Sagemaker, Step function, EKS, ECR, lambda functions, KMS, IAM, Rout53, ALB is mandatory for this role Familiarity with Sagemaker toolchain i.e. Studio, Groundtruth, Clarify, Comprehend, Pipeline, Bedrock etc..  Familiarity with dashboarding tools like Qlik, Tableau, Grafana, Kibana etc.  Familiar with machine learning techniques and advanced analytics (e.g. regression, classification, clustering, time series, econometrics, causal inference, mathematical optimization) is a plus.