Wholesale Credit Risk Associate - Product Strategy
JPMorgan Chase & Co. (company site, opens in a new tab)2+ yrsMumbaiOn-siteDirect career pageEst. 12–20 LPA
Posted 1d ago
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Reach people at JPMorgan Chase & Co.
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at JPMorgan Chase & Co. you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Develop, optimize, and maintain end-to-end scalable data pipelines and datasets ensuring cross-platform data consistency.
- Design and build generative AI pipelines integrating large language models, prompt engineering, and vector databases for context-aware applications.
- Lead development activities including stakeholder engagement, coding hygiene improvements, and support across the software development life cycle.
Minimum Requirements
- Master's degree in Computer Science, IT, Data Engineering or related field; or Bachelor's with 2 years relevant experience.
- Proficiency in Python (including Pandas, NumPy), SQL (window functions, stored procedures), and Tableau for data visualization.
- Experience with data engineering tools and platforms including Databricks, PySpark, AWS, Oracle PL/SQL, Microsoft SQL Server.
- Experience designing and implementing generative AI pipelines with LangChain, LlamaIndex, or Hugging Face; and working knowledge of vector databases (Pinecone, ChromaDB, FAISS).
Ideal Candidate Profile
- Experienced in full software development life cycle ownership including requirements gathering, testing, and support in data engineering contexts.
- Strong technical leadership skills demonstrated by leading development teams and effective stakeholder communication.
- Expertise in building complex, scalable data pipelines and AI-driven analytics solutions within a large financial institution setting.