Quantiphi
Architect - Platform Engineering - USA
USA - Remote
Sponsorship not specifiedDetected 30 days ago
PythonSQLBigQuerySnowflakeDatabricksAWSGCPKubernetesCI/CDPrometheusDevOpsPlatform EngineeringMachine LearningAirflowData EngineeringLLMsAgentic AILLMOpsMLOpsCybersecurityExcel
About the role
- While technology is the heart of our business, a global and diverse culture is the heart of our success.
- We love our people and we take pride in catering them to a culture built on transparency, diversity, integrity, learning and growth.
- If working in an environment that encourages you to innovate and excel, not just in professional but personal life, interests you- you would enjoy your career with Quantiphi!
Responsibilities
- Architect and implement the MLOps strategy for the program, ensuring alignment with the project proposal and delivery roadmap.
- Design and own enterprise-grade ML/LLM pipelines covering model training, validation, deployment, versioning, monitoring, and CI/CD automation using GCP-native services.
- Build container-oriented ML platforms (GKE-first) while evaluating alternative orchestration tools with similar capabilities (Kubeflow, Vertex AI, MLflow, Airflow, etc.).
- Collaborate with cross-functional teams - data engineering, platform, DevOps, and client stakeholders - to deliver production-ready ML solutions on Google Cloud.
Requirements
- Travel Required - upto 30%
Skills
- 10+ years working in ML/AI platform engineering or AI/MLOps roles with strong architecture exposure.
- Hands-on experience with MLOps toolset and awareness of: MLflow, Kubeflow, Vertex AI Pipelines, Airflow, BentoML, KServe, Seldon.
- Deep understanding of model lifecycle management (feature engineering -> training -> registry -> deployment -> monitoring).
- Experience implementing or supporting LLMOps pipelines, including prompt versioning, evaluation metrics, and automation frameworks.
- Strong experience with Google Cloud's Vertex AI platform, including Pipelines, Feature Store, Model Registry, and Model Monitoring.
- Experience implementing ML CI/CD pipelines including automated training, testing, validation, model promotion, and endpoint deployment.
- Strong SQL and data transformation experience using Snowflake, Databricks, Spark.
- Experience with feature engineering pipelines and Feature Store management.
- Understanding of lineage tracking: training data snapshot, feature versions, code versioning, metadata tracking, and reproducibility.
- Hands-on experience with Vertex AI Foundation Models, OpenAI, Anthropic, or Llama models.
- Experience with Cloud Monitoring, Vertex AI Model Monitoring, Prometheus/Grafana.
- Strong foundation in Python and cloud-native development patterns.
Benefits
- Exposure to the latest technologies related to artificial intelligence and machine learning, data and cloud
Company info
- Be part of the fastest-growing AI-first digital transformation and engineering company in the world
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This listing is sourced directly from Quantiphi's careers page and normalized into a canonical job model.