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Ocient builds infrastructure for AI agents that operate on massive datasets — their stack spans C++, Java, Spark, and data warehouses (Snowflake, BigQuery, Redshift, Azure) alongside ML frameworks (scikit-learn, XGBoost, Spark MLlib). Active projects reveal a company focused on closing gaps between ML capabilities and production frameworks, improving warehouse performance at scale, and designing reference architectures for cloud service providers. The engineering-heavy hiring profile (8 of 11 roles) paired with projects in infrastructure improvements and test reliability suggests a company investing heavily in making large-scale ML systems reliable and reproducible.
Tech Stack 43 technologies
Core StackC++ Java scikit-learn Scala Databricks Snowflake BigQuery Redshift AWS Kafka Python Elasticsearch InfluxDB Prometheus Grafana Dynatrace Ansible Puppet Jenkins GitLab CI/CD Spark MLlib XGBoost Azure GCP pytest Unix Graylog Zabbix Chef Bash+13 more
What Ocient Is Building
◆Challenges
- Balancing on-prem capacity economics
- Closing feature gaps
- Performance at scale
- Behavioral differences relative to frameworks
- High-volume telemetry ingestion
- Cloud data integration
- Reducing flaky tests
- Improving test coverage
- Enhancing test reliability
- High volume ticket handling
▲Active Projects
- Cutting-edge data warehouse
- Ml feature development for production workflows
- Gap analysis between ml capabilities and frameworks
- Ml infrastructure improvements
- Ocientaiq solution design
- Pilot execution and system sizing
- Reference architecture creation
- Batch and stream loading systems
- Streamloader
- Ocientaiq solutions for csp use cases
Hiring Activity
10 roles · 10 in 30d
Department
Seniority
Company intelligence