- Responsible for maturing our infrastructure from ECS & ElasticBeanstalk based on the needs of engineering, business, and security.
- Improve the rate at which our engineering team delivers code through the design and implementation of continuous delivery systems, including mentoring software engineers to take ownership of their deployments
- Define role-based access policies for granular control by engineers and data scientists
- Support monitoring and alerting needs by assisting in the design of systems to aggregate and organize critical logging data.
- Automate AWS cost reporting and advise the team on methods to optimize costs and understand spend
- Provide some support and administration for enterprise software (Jira, G Suite, etc.)
- Current stack is as follows, with the expectation that this role will be responsible for changing infrastructure-related tech as needed to support business objectives:
- Some Terraform, CircleCI, and Docker
- Ruby / Rails
- Some Python & Rust (ML / NLP)
- PostgreSQL (RDS)
- Redis (ElastiCache)
- 4+ years of experience working with cross-functional SRE/infrastructure/DevOps teams
- AWS experience (Route53, EC2, S3)
- Experience with containerization and related orchestration technology
- A BS/MS in computer science or related field of study, or equivalent experience
- Programming experience in one of: Ruby, Go, Python, Elixir, Java, or similar language
- Familiarity with networking technology (TCP/IP, VPNs, iptables, etc.)
- Ability to communicate ideas to technical and non-technical colleagues
- You have strong opinions about technology and the facts to back it up
- You welcome healthy but respectful debate
- You have designed and deployed infrastructure to support a small-to-medium sized software company or 10+ person engineering team
- The thought of code sitting undeployed for more than a week sends shivers up your spine
- You would automate getting dressed in the morning if you could
- You take serious levels of ownership for the systems in your care
VoiceOps uses AI to improve call center rep performance with world-class coaching.
Our average customer makes tens of thousands of calls per week. In a world without VoiceOps, they have literally no idea what their sales reps are doing on the phone. It's a total (and scary) black box.
By applying ML and a great UI to this problem, call center leadership has all the data they need about customer conversations at their fingertips, and can coach their reps more effectively and efficiently.
The technical problem is interesting, and gets more interesting as we grow. Our core challenge is how to take billions of audio recordings (and messy, unstructured human conversations) and make sense out of that data in a way that is: a) accurate b) cost efficient, and c) highly scalable. The corresponding product problem is how to take well-structured data and make it actionable for the end-user.
Call center recordings are one of the richest/largest untapped datasets in the world (literally, billions of calls stored in AWS buckets that no one is touching right now). We're going to be the best in the world at structuring that data and putting it to use to make businesses work better.