Applied AI ML Analyst

Shape the future of software development by building practical, generative artificial intelligence solutions that reach real users. Join a collaborative engineering team where you will learn modern delivery practices, grow your technical depth, and expand your career mobility. Bring your curiosity and builder mindset to help create reliable, secure capabilities that improve how teams work.

As an Applied AI ML Analyst at JP Morgan Chase, you will contribute to building, testing, and operating scalable generative artificial intelligence capabilities. You will partner with engineers and product partners to deliver production software components that support retrieval augmented generation and agent-based workflows. You will help improve reusable engineering components and standards to accelerate delivery across teams. You will support evaluation, monitoring, and operational readiness for deployed generative artificial intelligence systems.

Job Responsibilities

• Build software features and services that enable retrieval augmented generation experiences, including data ingestion, text segmentation, embedding generation, indexing, and retrieval.
• Implement agent-based workflow patterns, including planning and execution logic, tool integration, state management, retry handling, and error recovery.
• Contribute reusable engineering components by improving shared templates, software development kits, reference implementations, and developer documentation.
• Support evaluation and monitoring by helping create offline test sets, basic evaluation automation, telemetry dashboards, alerts, and regression checks integrated into deployment pipelines.
• Partner with agile teams to translate requirements into well-scoped technical tasks and participate in design reviews, code reviews, and testing.
• Operate delivered services by assisting with troubleshooting, debugging, and basic production support using logs and metrics.
• Apply secure engineering practices by following data handling controls, access permissions, and risk-aware deployment expectations.
• Promote a culture of inclusion, respect, and collaboration across teammates and stakeholders.

Required Qualifications, Capabilities, and Skills

• 2+ years of software engineering and/or data engineering experience (including internships, cooperative education, or substantial academic projects).
• Experience writing production-quality code in Python, Java, or a similar programming language, including automated tests.
• Experience building or supporting end-to-end services or pipelines, such as application programming interfaces, batch processing, streaming processing, or data workflows.
• Familiarity with operating services in a team environment, including logging, debugging, and basic production support concepts.
• Hands-on exposure to generative artificial intelligence concepts through projects, internships, or work experience (for example retrieval augmented generation, prompt orchestration, embeddings and retrieval, or evaluation approaches).
• Understanding of core data engineering concepts such as data quality, schema changes, backfills, and idempotent processing.
• Awareness of data governance and personally identifiable information handling considerations.
• Familiarity with cloud fundamentals and modern engineering practices such as version control, build pipelines, and container basics.
• Demonstrated ability to communicate clearly and collaborate with engineers and partner teams.

Preferred Qualifications, Capabilities, and Skills

• Bachelor’s or Master’s degree in Computer Science or equivalent practical experience.
• Familiarity with generative artificial intelligence orchestration frameworks and patterns for tool integration, retries, and safety guardrails.
• Coursework or project experience with machine learning frameworks such as PyTorch or TensorFlow.
• Exposure to evaluation automation for machine learning or generative artificial intelligence systems, such as test harnesses, offline metrics, or human review workflows.
• Exposure to Amazon Web Services deployment patterns, such as managed Kubernetes services, and basic cost and latency considerations.