Are you a Machine LearningOps Engineer working at a Large Financial Institution and being told by your leadership that you are too hands on or detail oriented or think and work like a start-up?

Imagine working at Intellibus to engineer platforms that impact billions of lives around the world. With your passion and focus we will accomplish great things together!

We are looking forward to you joining our Platform Engineering Team.

Our Platform Engineering Team is working to solve the Multiplicity Problem. We are trusted by some of the most reputable and established FinTech Firms. Recently, our team has spearheaded the Conversion & Go Live of apps which support the backbone of the Financial Trading Industry.

We are looking for Engineers who can

• Use Container Services & Registries to Serve ML Models in the Cloud
• Use Terraform & Jenkins to Setup Automated CI/CD Pipelines
• Train ML Models off of Real Time Streaming Data from Data Pipelines
• Setup Data Streaming from Kafka to Run ML Models on Real Time Data
• Develop Practices & Standards around ML Model Training On-Prem & In Cloud
• Create Portable & Deployable Assets for Sagemaker & ML Flow Models
• Add Observability & Monitoring to an Existing ML Model Deployed In Cloud
• Setup Observability & Monitoring during ML Model Training
• Shirk Models with 100mm+ Params to >100k Params for Edge Deployments
• Setup Dashboards to Showcase Model Quality (F1 Score, Precision, & Recall)
• Expose ML Models over REST APIs using Django / Flask / API Gateways

We work closely with

• Machine Learning
• DevOps
• MLOps
• CI
• CD
• ECS
• ECR
• Terraform
• Chef
• Jenkins
• REST APIs
• GitLab
• Git
• GitHub
• Python
• JavaScript
• jFrog

Our Process

• Schedule a 15 min Video Call with someone from our Team
• 4 Proctored GQ Tests (< 3 hours)
• 30-45 min Final Video Interview
• Receive Job Offer

If you are interested in reaching out to us, please apply and our team will contact you within the hour.

Schedule:

• Monday to Friday

Experience:

• DevOps: 5 years (Preferred)
• UNIX: 5 years (Preferred)
• AWS: 5 years (Preferred)