DescriptionWe have an opportunity to impact your career and provide an adventure where you can push the limits of whats possible.
As a Lead Software Engineer at JPMorgan Chase within the Wealth Management - Applied AI & Analytics team you play a crucial role in an agile team dedicated to enhancing building and delivering market-leading technology products that are secure stable and scalable. You are a key technical contributor responsible for implementing critical technology solutions across various technical domains to support the firms business objectives. Our team leverages cutting-edge machine learning techniques alongside the companys unique data assets to optimize business this position you will be part of our industry-leading data analytics team advancing financial applications from business intelligence generation to predictive models and automated decision-making. You will collaborate closely with financial advisors investors client service and operations.
Job responsibilities
- Executes creative software solutions design development and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems
- Develops secure high-quality production code and reviews and debugs code written by others
- Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems
- Leads evaluation sessions with external vendors startups and internal teams to drive outcomes-oriented probing of architectural designs technical credentials and applicability for use within existing systems and information architecture
- Leads communities of practice across Software Engineering to drive awareness and use of new and leading-edge technologies
- Collaborates with business stakeholders to formulate relevant financial and business questions that can be answered by data analysis.
- Researchs and analyzes data sets using a variety of statistical and machine learning techniques.
- Communicates final results and give context.
- Documents approach and techniques used.
- Works on longer term projects building tooling that can be used to scale certain types of analyses across multiple datasets and business use cases.
- Collaborates with internal machine learning teams.
Required qualifications capabilities and skills
- Formal training or certification on software engineering concepts and 5 years applied experience
- B.S. or M.S. in a quantitative discipline in Computer Science Mathematics Statistics Engineering Data Science or similar
- Experience with machine learning APIs and computational packages (examples: TensorFlow PyTorch Keras Scikit-Learn NumPy SciPy Pandas statsmodels).
- Strong ability to develop and debug in Python or similar professional programming language. Experience with big-data technologies and platforms such as Spark Snowflake etc.
- Background and experience in language model fine-tuning and building language models from scratch.
- Experience with common Generative AI software stack (i.e. HuggingFace LangChain FAISS DSPy etc.)
- Must have the ability to design or evaluate intrinsic and extrinsic metrics of your models performance which are aligned with business goals.
- Must be able to independently research and propose alternatives with some guidance as to problem relevance.
- Must be able to undertake basic and advanced EDA may require some direction from more senior team; should be aware of limitation and implication of methodology choices.
- Ensures re-use and sharing of ideas within team and locale.
- Able to work with non-specialists in a partnership model conveys information clearly and creates a sense of trust with stakeholders.
Preferred qualifications capabilities and skills