10 часов назад
Senior AI/ML Platform Engineer
148 000 - 247 000$
Вакансия из Hirify Global, списка международных tech-компанийДля мэтча и отклика нужен Plus
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
Senior AI/ML Platform Engineer (MLOps, Kubernetes, Cloud): Architecting and scaling a secure ML platform that supports the full machine learning lifecycle, from data ingestion and model training to monitoring, with an accent on distributed systems, cloud infrastructure, and workflow orchestration. Focus on building reproducible ML workflows, optimizing compute and storage performance, and establishing governance, CI/CD, and model observability practices.
Location: United States — San Mateo, California; hybrid environment
Salary: $148,000–$247,000 annual base salary
Company
provides cloud-based digital, core, analytics, and AI software for property and casualty insurers in 40 countries.
What you will do
- Architect and scale a secure ML platform supporting data ingestion, training, experimentation, model registration, deployment, and monitoring.
- Design infrastructure for hyperparameter tuning, experiment tracking, model registries, and reproducible ML workflows.
- Orchestrate ML workflows with tools such as Kubeflow, SageMaker, and MLflow.
- Build robust data pipelines with Data Engineers and optimize ML workloads across cloud compute and storage layers.
- Define best practices for governance, CI/CD, security, privacy, and regulatory compliance throughout the ML lifecycle.
- Lead technical discussions, establish the ML platform roadmap, and mentor junior engineers.
Requirements
- 10+ years of software engineering experience, including 5+ years working with ML platforms or infrastructure.
- Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
- Expertise in large-scale distributed systems and microservices.
- Strong programming skills in Python, Go, or Java.
- Experience with Docker, Kubernetes, AWS, GCP, or Azure.
- Advanced experience with MLOps tools such as MLflow, Kubeflow, SageMaker, Vertex AI, or Databricks, plus knowledge of statistical learning and deep learning.
Nice to have
- Experience with real-time model inference, streaming ML pipelines, Kafka, Flink, or Spark Structured Streaming.
- Knowledge of model governance, reproducibility, monitoring, drift detection, feature stores, Airflow, or Argo.
- Familiarity with model auditability, interpretability, CCPA, or GDPR.
- Experience with TeamCity, Terraform, or insurance, banking, and finance domains.
Culture & Benefits
- Flexible work environment in a collaborative hybrid setting.
- Health, dental, and vision insurance, wellness benefits, paid time off, and volunteer time off.
- Company-sponsored retirement plan and potentially eligible incentive programs.
- Continual development and internal career growth opportunities.
- In-person orientation for all roles.
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