ML Data Infrastructure Engineer
ML Data Infrastructure Engineer
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Salary Not Disclosed
1 Vacancy
Job Description
Candidates should have strong experience in Deep Leaning ML frameworks and model serving technologies.
Please get strong ML Serving/ infrastructure data scientist experience:
JD:
7 years of software engineering experience with 3 years in ML serving/infrastructure
Strong expertise in container orchestration (Kubernetes) and cloud platforms
Experience with model serving technologies (TensorFlow Serving Triton KServe)
Deep knowledge of distributed systems and microservices architecture
Proficiency in Python and experience with highperformance serving
Strong background in monitoring and observability tools
Experience with CI/CD pipelines and GitOps workflows
Role: ML Data Infrastructure Engineer
Location: Sunnyvale CA hybrid onsite
Duration: 12 Months
USC or GC Only
Key Responsibilities:
- Design and implement scalable data processing pipelines for ML training and validation
- Build and maintain feature stores with support for both batch and realtime features
- Develop data quality monitoring validation and testing frameworks
- Create systems for dataset versioning lineage tracking and reproducibility
- Implement automated data documentation and discovery tools
- Design efficient data storage and access patterns for ML workloads
- Partner with data scientists to optimize data preparation workflows
Technical Requirements:
- 7 years of software engineering experience with 3 years in data infrastructure
- Strong expertise in GCPs data and ML infrastructure:
- BigQuery for data warehousing
- Dataflow for data processing
- Cloud Storage for data lakes
- Vertex AI Feature Store
- Cloud Composer (managed Airflow)
- Dataproc for Spark workloads
- Deep expertise in data processing frameworks (Spark Beam Flink)
- Experience with feature stores (Feast Tecton) and data versioning tools
- Proficiency in Python and SQL
- Experience with data quality and testing frameworks
- Knowledge of data pipeline orchestration (Airflow Dagster)
Nice to Have:
- Experience with streaming systems (Kafka Kinesis)
- Experience with GCPspecific security and IAM best practices
- Knowledge of Cloud Logging and Cloud Monitoring for data pipelines
- Familiarity with Cloud Build and Cloud Deploy for CI/CD
- Experience with streaming systems (Pub/Sub Dataflow)
- Knowledge of ML metadata management systems
- Familiarity with data governance and security requirements
- Experience with dbt or similar data transformation tools
Employment Type
Full Time
