If you are looking for a game-changing career, working for one of the world's leading financial institutions, you’ve come to the right place.
As a Principal Software Engineer-KYC Risk Assessment at JPMorgan Chase within the Corporate Sector, you provide expertise and engineering excellence as an integral part of an agile data engineering team. To enhance, build, and deliver a trusted market leading Global Know Your Customer (KYC) and Risk Assessment Data Platform in a secure, stable, and scalable way.
Leverage your advanced technical capabilities and collaborate with colleagues across the organization to drive best-in-class outcomes across various technologies to support one or more of the firm’s portfolios.
This role is suited to a senior engineer who has hands-on skills to lead across multiple teams—defining architecture, engineering practices and standards, and delivering high-impact software that scales.
- Architects and implements complex, scalable engineering frameworks and solutions using modern software design principles
- Develops secure, high-quality production code for data-intensive applications and platforms, and reviews and mentors other engineers
- Creates durable, reusable software frameworks and patterns that are leveraged across teams and functions
- Designs and governs agentic AI systems, including multi-agent workflows, tool-use integrations, and human-in-the-loop controls appropriate for regulated financial services environments
- Establishes engineering standards for LLM-based applications — RAG pipelines, embedding workflows, vector store integrations, and model serving — ensuring safety, observability, and reproducibility at scale
- Drives adoption of advanced technical methods and practices aligned with the latest industry standards and product development methodologies
- Serves as the function's go-to subject matter expert in one or more areas of focus within data engineering, platform architecture, or AI systems
- Advises cross-functional teams on technological matters within your domain of expertise
- Influences leaders and senior stakeholders across business, product, and technology teams on technical strategy and direction
- Architects and governs agentic AI-enabled engineering workflows (using enterprise-authorized tools within the work environment) to improve delivery speed, code quality, and operational outcomes at scale (e.g., AI-driven PR review assistance, test generation/maintenance, release readiness checks, incident triage and root-cause acceleration), while defining guardrails for validation, security, resiliency, and reuse across teams.
- Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation at scale.
- Formal training or certification on software engineering concepts and 7+ years applied experience
- Hands-on practical experience delivering system design, application development, testing, and operational stability at enterprise scale
- Hands-on experience designing and deploying production AI/ML systems, including LLM-based applications and agentic architectures with tool use, memory, and multi-step reasoning in regulated environments
- Expert in one or more programming languages, particularly Python and/or Java
- Advanced knowledge of software application development and technical processes, with considerable depth in one or more disciplines (e.g., cloud, AI/ML, data engineering)
- Experience in large-scale data processing, microservices, API design, Kafka, Redis, MemCached, observability tools (Dynatrace, Splunk, Grafana), and orchestration frameworks (Airflow, Temporal)
- Advanced working knowledge of relational and NoSQL databases, vector stores, data lake architectures, and data governance
- Practical cloud-native experience (AWS, Azure, or GCP)
- Ability to present and effectively communicate with senior leaders and executives
- Demonstrable experience designing and leading adoption of agentic AI-enabled development practices (using enterprise-authorized tools within the work environment) across teams, including setting standards for human-in-the-loop validation, auditability/traceability of changes, and secure handling of sensitive data.
- Strong understanding of responsible AI use and control expectations in engineering workflows, including security/resiliency implications, data sensitivity, and risk-based governance; ability to influence senior technical leaders on safe scaling patterns and reuse.
- Experience with modern data platforms such as Databricks or Snowflake
- Deep hands-on experience with Spark/PySpark and other big data processing technologies
- Expertise in open-source table formats and catalog services such as Apache Iceberg
- Experience with LLM orchestration frameworks and model serving infrastructure or managed endpoints (AWS Bedrock, Azure OpenAI)
- Familiarity with AI evaluation and observability practices: evals frameworks, red-teaming, prompt drift detection, and cost/latency monitoring for LLM workloads
- Understanding of agentic design patterns and how to constrain agent autonomy in high-stakes financial workflows
- Awareness of AI risk and regulatory considerations relevant to AI use in financial decision-making