Lead AI Platform Engineer — MLOps & Observability

TechDigital Group; San Jose (CA); Sur place; USD 140 000 - 210 000; Plein temps

Résumé du poste

TechDigital Group is seeking an AI Engineer to lead ML sustenance across a multi-service portfolio, acting as the onsite technical point of contact and mentoring a globally distributed team.

You will design and sustain automated training pipelines, data ingestion, and feature engineering, implement robust evaluation, deployment, and observability, and drive cost-aware, compliant AI solutions.

Qualifications

• 8+ years in software, ML engineering, or MLOps/DevOps, incl. 2+ years in a tech lead role.
• Experience with GCP MLOps services (Vertex AI, Kubeflow) or open-source ecosystems (Airflow, MLflow).
• Proficiency in Python or Go and large-scale data processing.
• Experience with production deployments, container orchestration, and CI/CD pipelines.
• Experience with distributed systems monitoring, SLOs, on-call incidents, and root-cause analysis.
• Ability to lead and mentor engineers across time zones and communicate with stakeholders.

Responsabilités

• Set technical direction and roadmap for ML sustenance across a multi-service portfolio; mentor a globally distributed team.
• Design, build, and sustain automated continuous training, data ingestion, and feature engineering pipelines.
• Configure evaluation pipelines, golden datasets, and dashboards; set up alerting for drift or failures.
• Build and maintain CI/CD workflows for safe deployment and rollback of containerized ML models; ensure observability.
• Enforce data isolation, IAM/RBAC, and strict data retention/deletion policies across tenants.
• Develop AI-assisted automation to scale operations and optimize compute/token costs.

Connaissances

ML lifecycle

GCP Vertex AI

Python

Go

Kubernetes

Docker

CI/CD

Distributed systems

Stakeholder leadership

Outils

Kubeflow

MLflow

Apache Airflow

Jenkins

GitLab CI

Kubernetes

Docker

Description du poste

TechDigital Group is seeking an AI Engineer to lead ML sustenance across a multi-service portfolio, acting as the onsite technical point of contact and mentoring a globally distributed team.

You will design and sustain automated training pipelines, data ingestion, and feature engineering, implement robust evaluation, deployment, and observability, and drive cost-aware, compliant AI solutions.