AI/ML Software Engineer III

Testing Site - JPMC; Ohio; On-site; USD 120,000 - 170,000; Full time

Job summary

JPMorganChase in Ohio seeks a Software Engineer III to build and continuously improve production-grade AI software, partnering with advisors, client service, product, and risk teams to translate needs into measurable outcomes. You will scale responsible AI capabilities across multiple use cases.

You will develop data pipelines, governance practices, and retrieval-augmented generation systems, contributing to the AI-enabled transformation of financial services with strong software engineering

Qualifications

• 3+ years of applied software engineering experience.
• Formal training or certification in software engineering.
• Strong Python and data management fundamentals.
• Experience with ML/data pipelines and AI product use cases.
• Experience collaborating with risk and control functions.

Responsibilities

• Prepare and manage data for AI products by sourcing, understanding, and curating structured and unstructured datasets for generative AI and machine learning applications.
• Build and maintain data pipelines supporting retrieval-augmented generation and analytics, including ingestion, parsing, chunking, metadata tagging, indexing, and transformations.
• Diagnose and resolve data issues by identifying root causes and coordinating remediation with upstream teams.
• Implement data quality checks, profiling, validation, reconciliation, and monitoring to detect issues early and prevent production regressions.
• Support governance and controls by following data handling requirements and documenting sources, definitions, and assumptions.
• Collaborate with stakeholders to translate business needs into scoped technical approaches with measurable success criteria.
• Develop with modern generative AI techniques, including retrieval-augmented generation, prompt design, agentic workflows, evaluation frameworks, and safety guardrails.
• Use AI-assisted development tools as part of your daily workflow and contribute to team best practices for AI-augmented engineering.
• Communicate system behavior, trade-offs, and business impact clearly to both technical and non-technical audiences.
• Document designs, experiments, and decisions rigorously, including validation evidence and reproducibility details.
• Build reusable tooling and infrastructure to scale AI delivery across use cases.

Skills

Python

SQL

Docker

REST APIs

CI/CD

Testing

Version Control

Data Pipelines

Data Governance

Communication

Tools

PyTorch

TensorFlow

Hugging Face

Job description

You will join an industry-leading team building production-grade AI-powered software, from intelligent retrieval and automation systems to generative AI tools that augment financial advisors, investors, and operations teams.

As a Software Engineer III at JPMorganChase within Applied AI and Machine Learning, you will build, deliver, and continuously improve real software that solves real problems. You will partner closely with financial advisors, client service, product, operations, and risk and control teams to translate ambiguous needs into measurable outcomes, and you will help scale responsible, well-governed AI capabilities across multiple use cases.

Job responsibilities

• Prepare and manage data for AI products by sourcing, understanding, and curating structured and unstructured datasets for generative AI and machine learning applications.
• Build and maintain data pipelines supporting retrieval-augmented generation and analytics, including ingestion, parsing, chunking, metadata tagging, indexing, and transformations.
• Diagnose and resolve data issues by identifying root causes (for example, missing data, duplicates, schema changes, and inconsistent definitions) and coordinating remediation with upstream teams.
• Implement data quality checks, profiling, validation, reconciliation, and monitoring to detect issues early and prevent production regressions.
• Support governance and controls by following data handling requirements (access, retention, privacy, and security) and documenting sources, definitions, and assumptions.
• Collaborate with stakeholders to translate business needs into scoped technical approaches with measurable success criteria.
• Develop with modern generative AI techniques, including retrieval-augmented generation, prompt design, agentic workflows, evaluation frameworks, and safety guardrails.
• Use AI-assisted development tools as part of your daily workflow and contribute to team best practices for AI-augmented engineering.
• Communicate system behavior, trade-offs, and business impact clearly to both technical and non-technical audiences.
• Document designs, experiments, and decisions rigorously, including validation evidence and reproducibility details.
• Build reusable tooling and infrastructure (shared libraries, evaluation harnesses, prompt libraries, and pipelines) to scale AI delivery across use cases.

Required qualifications, capabilities and skills

• Formal training or certification on software engineering concepts and 3+ years applied experience.
• Proficiency in Python and strong software engineering fundamentals, including testing, version control, code review, continuous integration/continuous delivery, and writing maintainable production-quality code.
• Strong SQL and data management capability, including dataset comprehension, data profiling and quality checks, and diagnosing pipeline and data issues (for example, schema drift and inconsistent metrics).
• Experience building or supporting data pipelines for analytics or machine learning workflows.
• Familiarity with containerization and service fundamentals (for example, Docker and REST-based services).
• Practical experience with modern generative AI approaches, such as large language model APIs, retrieval-augmented generation architectures, embeddings, vector search, tokenization concepts, and evaluation of generative outputs.
• Understanding of responsible AI concepts, including privacy, security, and guardrails, and the ability to partner effectively with risk and control functions.
• Strong communication and collaboration skills across engineering, product, operations, and governance partners.

Preferred qualifications, capabilities and skills

• Exposure to distributed data processing patterns (for example, Spark).
• Familiarity with deep learning frameworks and ecosystems (for example, PyTorch, TensorFlow, and Hugging Face).
• Knowledge of financial markets, wealth management products, or advisor and client workflows.
• Demonstrated builder mindset through open-source contributions or personal projects in AI and machine learning.