Software Engineering III – AI/ML Engineer

As a Site Reliability Engineer for AI/ML Data Platforms, you will be instrumental in building scalable, resilient, and market‑leading data solutions. You will engage in root‑cause analysis, production changes, budgetary considerations, and staffing challenges. Your experience will be vital in managing and mentoring team members to drive strategic change, both within your team and in partnership with colleagues across JPMorgan Chase & Co.’s global network of innovators.

Responsibilities

• Coordinate incident management coverage to ensure effective resolution of application issues.
• Collaborate with cross‑functional teams to perform root‑cause analysis and implement production changes.
• Leverage enterprise‑authorized AI coding‑assist tools to improve code quality, delivery speed, and productivity across complex deliverables (e.g., code generation/refactoring, unit test creation, documentation). Validate outputs through peer review, automated testing, and secure coding standards, and contribute learnings and reusable patterns to improve broader team effectiveness.
• Apply 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.
• Develop and support AI/ML solutions for troubleshooting and incident resolution.
• Exercise proficiency in site reliability culture and principles and familiarity with implementing site reliability within an application or platform.
• Run production incident calls and manage incident resolution.
• Use observability practices such as white‑box and black‑box monitoring, service‑level objective alerting, and telemetry collection with tools such as Grafana, Dynatrace, Prometheus, Datadog, and Splunk.
• Understand SLI/SLO/SLA and error budgets and apply them to service reliability.
• Critically evaluate, validate, and refine AI‑generated outputs for correctness, performance, and security.
• Advise on responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations.
• Use Python or PySpark for AI/ML modeling.
• Reduce toil by building new tools to automate repeated tasks.
• Apply hands‑on experience in system design, resiliency, testing, operational stability, and disaster recovery.
• Ensure awareness of risk controls and compliance with departmental and company‑wide standards.
• Collaborate effectively in teams and build meaningful relationships to achieve common goals.

Qualifications

• 4+ years in an SRE or production support role with AWS Cloud, Databricks, Snowflake, or similar technologies.
• AWS and Databricks certifications.

#J-18808-Ljbffr…