JP Morgan Chase is hiring an Applied AI ML Analyst in Bengaluru for candidates with 2+ years of software or data engineering experience, including internships, cooperative education or substantial academic projects. The role involves building production-ready generative AI capabilities for retrieval augmented generation and agent-based workflows, using technologies such as Python or Java, data pipelines, embeddings and cloud engineering practices. You will work with engineers and product partners on services used by real teams, while gaining exposure to modern software delivery, evaluation and operational support in a large global banking organisation.
Company Overview
JPMorganChase is a financial services organisation serving consumers, small businesses, corporations, institutions and government clients across more than 100 countries. Its businesses include investment banking, commercial banking, consumer banking, payments, securities services and asset management, making it a substantial name to add to a technology professional’s CV.
| Company Name | JP Morgan Chase |
| Job Role | Applied AI ML Analyst |
| Qualification | Bachelor’s or Master’s degree in Computer Science or equivalent practical experience |
| Job Location | Bangalore |
| Experience | 2+ years of software engineering and/or data engineering experience, including internships, cooperative education or substantial academic projects |
| Employment Type | Full Time |
Role Overview
The Applied AI ML Analyst will join an engineering team in the Commercial & Investment Bank and help deliver reliable, secure generative AI software for production use. The work covers both development and operational readiness rather than only experimentation.
- Build and support scalable generative AI capabilities for retrieval augmented generation and agent-based workflows.
- Work with engineers and product partners to turn requirements into production software components.
- Improve reusable engineering standards, templates, software development kits and reference implementations.
- Support evaluation, monitoring and deployment readiness for generative AI systems.
Eligibility & Qualifications
- At least 2 years of software engineering and/or data engineering experience are required.
- Internships, cooperative education and substantial academic projects can be included in the required experience.
- A Bachelor’s or Master’s degree in Computer Science, or equivalent practical experience, is preferred.
Key Responsibilities
- Build software features for retrieval augmented generation, including data ingestion, text segmentation, embedding generation, indexing and retrieval.
- Implement agent-based workflow patterns covering planning, execution logic, tool integration, state management, retries and error recovery.
- Improve shared templates, software development kits, reference implementations and developer documentation.
- Create offline test sets, basic evaluation automation, telemetry dashboards, alerts and regression checks for deployment pipelines.
- Participate in design reviews, code reviews and testing with agile teams.
- Assist with troubleshooting, debugging and basic production support using logs and metrics.
- Follow data handling controls, access permissions and risk-aware deployment practices.
- Promote inclusion, respect and collaboration with teammates and stakeholders.
Skills Required
- Production-quality coding in Python, Java or a similar programming language, including automated testing.
- Experience building or supporting end-to-end services or pipelines such as APIs, batch processing, streaming processing or data workflows.
- Hands-on exposure to generative AI concepts, including retrieval augmented generation, prompt orchestration, embeddings, retrieval or evaluation approaches.
- Understanding of data engineering concepts such as data quality, schema changes, backfills and idempotent processing.
- Awareness of data governance and personally identifiable information handling.
- Familiarity with logging, debugging, basic production support, version control, build pipelines and container fundamentals.
- Clear communication and the ability to collaborate with engineers and partner teams.
- Familiarity with generative AI orchestration frameworks, tool integration, retries and safety guardrails is preferred.
- Coursework or project experience with PyTorch or TensorFlow is preferred.
- Exposure to evaluation automation, offline metrics or human review workflows for machine learning or generative AI systems is preferred.
- Exposure to Amazon Web Services deployment patterns, managed Kubernetes services, cost considerations and latency considerations is preferred.
Why Join JP Morgan Chase
The role offers practical experience in taking generative AI systems from engineering and evaluation through deployment and basic production support. The listing also highlights modern delivery practices, technical depth, career mobility and collaboration with engineering and product teams, giving candidates exposure beyond model experimentation.
How to Apply for JP Morgan Chase Hiring 2026
Eligible and Interested candidates can apply using the below-mentioned link before the link expires. Applicants need to register with the company portal in order to complete the registration process.
Apply Link: Click Here To Apply
