Senior/Staff ML Ops Engineer
Posted on 7/19/2023
INACTIVE
Balbix

51-200 employees

AI-powered platform for cybersecurity risk reduction
Company Overview
Balbix stands out as a leader in the cybersecurity industry, offering a unique approach to risk reduction by utilizing AI to identify and mitigate the most critical cybersecurity issues. Their Security Cloud platform not only automates inventory of cloud and on-prem assets but also provides a unified view of cyber risk in monetary terms, enabling data-driven decision making. The company's recognition in CNBC's 2022 list of Top 25 Startups for the Enterprise and ranking #32 on the 2021 Deloitte Fast 500 North America, along with its growing clientele of Fortune 500 companies, attests to its competitive advantage and industry leadership.
AI & Machine Learning
Data & Analytics
Cybersecurity

Company Stage

Series C

Total Funding

$101.6M

Founded

2015

Headquarters

San Jose, California

Growth & Insights
Headcount

6 month growth

5%

1 year growth

24%

2 year growth

58%
Locations
San Jose, CA, USA
Experience Level
Entry
Junior
Mid
Senior
Expert
Desired Skills
Microsoft Azure
Python
NoSQL
SQL
Java
Postgres
Elasticsearch
MongoDB
Cassandra
Google Cloud Platform
CategoriesNew
AI & Machine Learning
Applied Machine Learning
Operations & Logistics
Requirements
  • MS/BS +3 years in Computer Science or a related field
  • Expert programming experience with Python or Java
  • 2 years experience in one of the ML OPs tools like Apache Airflow(Preferred)/Luigi/Argo/ML Flow/Kube Flow/Sage Maker
  • 2+ experience in working with any of the public cloud AWS(preferred)/Azure/GCP
  • Knowledge of SQL databases such as Postgres and NoSQL databases such as MongoDB, Cassandra, Redis
  • Experience working with Columnar Databases and JSON/Parquet/ORC message formats
  • Experience with search engine database such as ElasticSearch is an added bonus
Responsibilities
  • Design and implement automation of model training/handling feedback loop process (continuous training)
  • Implement solutions for continuous observability of ML models and their performance
  • Implement solutions for automation of ML Pipeline components deployment process with appropriate support for quality gate
  • Implement solutions for end to end tracking of model KPIs for a given dataset that support model versioning
  • Develop Platform and Modular approach in assisting quicker development of ML Models, including solutions for feature store, parameter store and time series data
  • Design solutions that are scalable and reduce time for ML model migration to prod
  • Build production quality solutions that are highly scalable and meet acceptance criteria of technical requirements
  • Interface with multiple teams, including ML, UI, backend and data engineering to ensure seamless data sourcing, handling, processing and visualizations.