Lead AI/ML Platform Engineer — Multi-Cloud & Auto-Remediation

JPMorgan Chase & Co.; Plano (TX); On-site; USD 130,000 - 190,000; Full time

Job summary

JPMorgan Chase & Co. is seeking a Lead Software Engineer to design, build, and deploy an AI solution that unifies observability data across AWS, Azure, and GCP. You will lead integration efforts for telemetry data, implement self-healing automation, and drive AI-assisted engineering practices across the team.

The role focuses on bridging ML, multi-cloud infrastructure, and automated IT operations to reduce incidents and improve system reliability at scale.

Qualifications

• Formal training or certification on software engineering concepts.
• 5+ years applied experience in software engineering or related field.
• Strong proficiency in Python and Java; experience with ML/LLM integration.

Responsibilities

• Build and deploy infrastructure solutions for seamless integration of control plane and user accounts.
• Design pipelines to ingest, aggregate, and correlate telemetry data from multi-cloud infrastructures.
• Architect and implement closed-loop automation playbooks for auto-remediation of common failures.
• Drive team adoption of enterprise-authorized AI-assisted engineering practices with secure validation standards.
• Apply SDLC tooling knowledge to improve automation value across the team.
• Build and operationalize LLM/ML models for anomaly detection and health forecasting.
• Integrate AI engine with ticket data and map observations to ticket trends.

Skills

Python

Java

LLM integration

Multi-cloud

Observability

Automation

APIs & Webhooks

ServiceNow/Jira

Tools

OpenTelemetry

Prometheus

Dynatrace

Snowflake

Job description

JPMorgan Chase & Co. is seeking a Lead Software Engineer to design, build, and deploy an AI solution that unifies observability data across AWS, Azure, and GCP. You will lead integration efforts for telemetry data, implement self-healing automation, and drive AI-assisted engineering practices across the team.

The role focuses on bridging ML, multi-cloud infrastructure, and automated IT operations to reduce incidents and improve system reliability at scale.