We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible
As an Applied AI/ML Engineer at JPMorgan Chase within the Global Banking Technology team, you will tackle complex business problems and apply algorithms to design, evaluate, and implement AI/ML applications or models. You will leverage the company's data assets using tools like Python, Spark, and AWS. Additionally, you will extract business insights from technical results and communicate them effectively to a non-technical audience.
Job Responsibilities:
- Contribute to platform engineering to support the Gen AI platform and develop Gen AI use cases.
- Assist in building machine learning pipelines using Generative AI models on AWS.
- Apply best practices in software engineering, MLOps, and data governance.
- Participate in code check-ins every sprint to support continuous integration and development.
- Ensure AI/ML solutions adhere to data privacy and security regulations.
- Communicate technical concepts and solutions across various organizational levels.
- Engage in the model development process, including data wrangling, analysis, model training, testing, and selection.
- Present insights from data analysis and modeling exercises to diverse audiences.
- Collaborate with engineers to build and deploy solutions for business requirements.
- Develop an understanding of key business problems and processes.
- Work with the OpenAI API, including prompt engineering and vector databases.
Required Qualifications, Capabilities, and Skills:
- Proficiency in Python with familiarity in both LLM Agent orchestration frameworks (e.g., Lang Graph, Langchain) and general machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, Scikit-learn).
- Experience in data wrangling, including cleaning and reshaping complex datasets using Python.
- Practical experience with supervised and unsupervised ML projects.
- Understanding of software engineering, AI/ML, MLOps, and data governance.
- Experience with AWS cloud services, including ECS, S3, Lambda, and SageMaker.
- Basic NLP skills, including tokenization and sentiment analysis.
- Experience in anomaly detection techniques and applications, with familiarity with LLM telemetry tools like Phoenix or Deepeval.
- Problem-solving, communication, and teamwork skills.
- Ability to work collaboratively with cross-functional teams.
Preferred Qualifications, Capabilities, and Skills:
- Experience with big data frameworks, such as Databricks.
- Familiarity with databases, like MySQL, Aurora PostgreSQL, etc.
- Experience with version control systems like GitHub.
- Exposure to graph analytics and neural networks.
- Experience working with engineering teams to operationalize machine learning models.
