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 at JPMorganChase within AMDP/CDAO, you will be a hands-on engineer who co-develops with Lines of Business application teams to deliver AI Foundation Services capabilities that make GenAI/AI workloads easier to build, safer to run, and faster to operate at scale. You will translate Lines of Business requirements into clear technical designs, implement and harden reusable platform components, and support services through launch and early-life operations with strong observability, Service Level Objective(SLOs), and runbooks. You will collaborate across product, platform, and security partners to ensure solutions meet firm standards while promoting reuse via practical reference patterns and engineering playbooks.
Job responsibilities
- Partners with Lines of Business application teams to design and co-develop AI Foundation Services capabilities that unblock GenAI/AI use cases (design, build, launch early ops).
- Implements reusable platform services and libraries (APIs/SDKs) that standardize how teams consume model hosting, inference, and AI/ML managed services
- Converts Lines of Business requirements (functional, non-functional) into implementable technical designs, breaking work into milestones and driving delivery to readiness gates
- De-risks delivery through performance, scale, reliability, and security engineering (capacity planning, load testing, resiliency patterns, secure-by-design controls)
- Produces and maintains reference architectures, runbooks, test harnesses, and baseline configurations (e.g., GPU training/serving defaults) to drive reuse across teams
- 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.
- Creates complex and scalable coding frameworks using appropriate software design frameworks
- Advises cross-functional teams on technological matters within your domain of expertise while serving as the function’s go-to subject matter expert
- Contributes to the development of technical methods in specialized fields in line with the latest product development methodologies
- Influences leaders and senior stakeholders across business, product, and technology teams
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 10+ years applied experience
- Strong hands-on coding ability in one or more languages used for platform services (e.g., Python, Java, Go), with proven delivery of production-grade APIs
- Experience building shared services/platform components with Terraform to be used by multiple application teams, including versioning, backward compatibility, and developer enablement
- Demonstrated 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
- Demonstrated ability to operate what you build: instrumentation/observability, Service Level Objective(SLOs), incident response, root-cause analysis, and continuous hardening
- Proven cross-team execution skills: can align stakeholders, manage dependencies, and drive outcomes with clear technical documentation and delivery plans
- Hands-on practical experience delivering system design, application development, testing, and operational stability
- Advanced knowledge of software application development and technical processes with considerable in-depth knowledge in one or more technical disciplines (e.g., cloud, artificial intelligence, machine learning, mobile, etc.)
- Experience applying expertise and new methods to determine solutions for complex technology problems in one or more technical disciplines
- Ability to present and effectively communicate with Senior Leaders and Executives
Preferred qualifications, capabilities, and skills
- Experience with GPU-enabled platforms and optimizing AI workloads (training/inference performance tuning, throughput/latency, cost optimization)
- Experience with model serving/model gateway patterns, rollout strategies, and safety controls (e.g., rate limiting, authN/Z, policy checks, evaluation hooks)
- Experience creating “golden paths” for teams (templates, reference implementations, automated tests) that measurably increase reuse and reduce time-to-launch
- Familiarity with regulated-environment engineering practices (threat modeling, auditability, data controls, secure SDLC) in large enterprises