Lead Software Engineer - GCP ML Engineer

JPMorgan Chase•Wilmington, DE

About The Position

We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible. As a Lead Software Engineer at JPMorgan Chase within the Chief Data and Analytics Office, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm's business objectives. Job responsibilities Execute creative software solutions, design, development, and troubleshooting for ML pipelines and services. Develop secure, high‑quality production code; review and debug ML pipeline, data processing, and inference code. Identify opportunities to eliminate or automate remediation of recurring ML pipeline issues to improve stability. Lead evaluation sessions with internal teams on ML architectures, scalability, and observability. Lead communities of practice around Vertex AI Pipelines, Feature Store, and MLOps best practices. Add to team culture of diversity, opportunity, inclusion, and respect. Design ML pipelines on Vertex AI Pipelines; automate ingestion, feature engineering, training, and deployment with reproducibility. Build and manage Feature Store and Model Registry; support fine‑tuning and online/batch inference at scale. Configure robust monitoring for model/data drift, security telemetry, and pipeline reliability; enforce alerting and SLOs. Implement encryption (CMEK/KMS), RBAC, org policies, and compliance controls across ML pipelines and artifacts.

Requirements

Formal training or certification on software engineering concepts and 5+ years applied experience.
Hands‑on experience in system design, application/ML development, testing, and operational stability.
Advanced proficiency in one or more programming languages ( Python required).
Proficiency in automation and continuous delivery for ML pipelines.
Proficient in all aspects of the Software Development Life Cycle and MLOps practices.
Advanced understanding of CI/CD, application resiliency, and security in ML systems.
Practical cloud‑native experience (GCP) operating data/ML workloads.
Demonstrated experience with Vertex AI Pipelines, Workbench, Predictions, and Feature Store.
Experience with model registry, drift detection, and observability (metrics/traces/logs) for ML services.
Ability to implement CMEK/KMS, org policy guardrails, and automated remediation controls.

Nice To Haves

Experience with Vertex AI Search, Vector Search, and RAG pipelines.
Familiarity with Gemini model families and evaluation instrumentation.
Strong data engineering for encryption, tagging/labeling, and lineage.
Competence in resource tagging aligned to Atlas 2.0 for security/finance/logging.
Experience with cost management dashboards and spend alerting for ML workloads.
Awareness of the GCP enablement roadmap (Florence/Atlas 2.0, JET integration).

Responsibilities

Execute creative software solutions, design, development, and troubleshooting for ML pipelines and services.
Develop secure, high‑quality production code; review and debug ML pipeline, data processing, and inference code.
Identify opportunities to eliminate or automate remediation of recurring ML pipeline issues to improve stability.
Lead evaluation sessions with internal teams on ML architectures, scalability, and observability.
Lead communities of practice around Vertex AI Pipelines, Feature Store, and MLOps best practices.
Add to team culture of diversity, opportunity, inclusion, and respect.
Design ML pipelines on Vertex AI Pipelines; automate ingestion, feature engineering, training, and deployment with reproducibility.
Build and manage Feature Store and Model Registry; support fine‑tuning and online/batch inference at scale.
Configure robust monitoring for model/data drift, security telemetry, and pipeline reliability; enforce alerting and SLOs.
Implement encryption (CMEK/KMS), RBAC, org policies, and compliance controls across ML pipelines and artifacts.