Minimum Qualifications:
• 8+ years of experience in software engineering, ML engineering, or MLOps/DevOps, focusing on the ML lifecycle (continuous training, model deployment, and continuous monitoring), including 2+ years in a technical lead role.
• Strong experience with Google Cloud Platform (Google Cloud Platform) MLOps services (Vertex AI, Kubeflow) or equivalent open-source ecosystems (Apache Airflow, MLflow).
• Proficiency in programming languages such as Python or Go, and experience with large-scale data processing and distributed systems.
• Experience working with large-scale production deployment, container orchestration (e.g., Kubernetes, Docker), and CI/CD pipelines (e.g., Jenkins, GitLab CI).
• Experience with production operations for distributed systems: monitoring and alerting, SLO definition, on-call incident triage, and root-cause analysis.
• Proven ability to design and implement end-to-end ML pipelines for data management, model training, evaluation, and deployment.
• Demonstrated ability to lead and mentor engineers across time zones, communicate clearly with technical and non-technical stakeholders, and manage multiple partner priorities.
Preferred Qualifications:
• Familiarity with Large Language Models (LLMs) and open-source Agentic AI frameworks (e.g., LangChain, AutoGen, LlamaIndex, or internal equivalent like Agent Development Kit).
• Knowledge of standard ML evaluation metrics for Generative and Classification models (e.g., BLEURT, ROUGE, Precision/Recall, AUC, Trajectory Exact Match).
• Experience managing highly sensitive data (e.g., PII/PHI) and implementing strict access control and encryption mechanisms.
• Strong analytical and debugging skills, with the ability to troubleshoot complex, distributed ML systems and infrastructure dependencies.
• Experience monitoring model quality in production, including data/concept drift detection, A/B testing, and building quality dashboards (e.g., Grafana, Datadog)
• Experience running knowledge-transfer and service-transition programs, or working in a sustenance, support-engineering, or SRE organization.
