About Us
Trail is the context layer AI agents run on. We're building the missing layer between your company's rules, exceptions, and institutional knowledge and the agents trying to act on them. It's a governed graph that lets any agent or platform, including Trail agents, make the right call on your most complex business processes. Agents today fail not because models are weak, but because they're never given the judgment a business builds up over years. We're fixing that.
Visit our Website and Youtube to learn more!
About the Role
As a senior fullstack engineer, you should be comfortable with both backend (Preferably Golang) and frontend (Preferably React). You will be primarily working on architecting and shipping new backend features like new integrations, enabling more machine learning API’s, building complex workflows & various growth hacking efforts. You will also work on optimizing response times, building features that will scale to 100’s of millions of documents we process every month.
What we expect from you
- Agility in shipping features
- Good at code design and architecture
- Great communication
- Backend experience - Preferably golang, python
- Databases - Understanding of data modeling for Nosql preferably cassandra.
- Strong fundamentals in OOP design patterns.
- Basic understanding of Devops.
- Curiosity and Willingness to learn new things while solving a challenging problem.
- Learning and incorporating best practices in software development, security and design/architecture.
What We're Looking For
- B.E/B.Tech preferable from a Tier 1 college
- 3-8 yrs in software development with a strong foundation in software architecture and design as well one of frontend and backend development
- Proficiency in programming and development technologies.
- Strong familiarity with DevOps best practices and tools.
- Innovative mindset and a passion for driving engineering excellence.
Some of the interesting features we have shipped in backend
- Compile python code into C which could be imported into golang and then shipped as binary for on premise systems
- Autoscale GPU dependent services with kubernetes with a custom metric
- Displaying machine learning metrics in simplified ways to end users so they can act based on those metrics
- Building large number and variety of integrations with relatively generic interface like salesforce, quickbooks, RPA's, external databases
- Process large number of files in highly distributed manner in golang
- Intelligent lookups leveraging vector databases with data synced from ERP Systems
Some of the interesting things we have shipped in frontend -
- Ability for users to annotate documents so AI can learn which fields to extract.
- Displaying machine learning metrics in simplified ways to end users so they can act based on those metrics
- Letting users build complex visual workflows around our API in our product.
- Let users visualize complex ML metrics in a very simple and intuitive way
Our Tech Stack
- Databases
- Cassandra DB
- Postgres/MySQL
- Backend
- Golang for API and other microservices
- Python for Machine learning (Tensorflow, Pytorch)
- Frontend
- React, Typescript
- Mobx
- Cloud Providers
- AWS
- GCP for ML heavy workload
- Monitoring/Alerting
- ELK for logging
- Prometheus for Monitoring
- Graphana for dashboards
- Orchestration
- Kubernetes
- DevOps
- Jenkins for CI/CD
Live on Trail's board as of 15 Nov. We check it every day.
Apply on Ashby ↗