Software Engineer

Concentrate provides one OpenAI-compatible API for accessing and managing AI at scale. Teams can access models across leading providers and open-source infrastructure through a single endpoint, select the best / fastest / most cost efficient model for each use case, improve reliability, lower token spend, strengthen data protection and security, and meet enterprise compliance requirements with built-in tooling.

Backed by top-tier investors.

Role Description

You’ll help build and improve production systems across our platform. This includes backend services, APIs, internal tooling, reliability, and troubleshooting customer issues.

We’re looking for someone who is technically strong, eager to learn, and excited to work in an early-stage environment. You should be comfortable taking ownership of projects with support from the team, moving quickly, and solving real problems that matter to customers.

What You’ll Do

* Build and improve backend services and APIs that support our unified LLM API

* Help improve system reliability, latency, and cost-efficiency across multiple model providers

* Contribute to internal tooling, logging, observability, and debugging workflows

* Investigate and fix customer issues across the stack, from API behavior to infrastructure and provider performance

* Support engineering best practices for testing, monitoring, and safe deployments

* Collaborate closely with the founding team on product and technical priorities

What We’re Looking For

* 0–3 years of experience in software engineering, including internships or strong personal/projects experience

* Strong foundations in computer science and software engineering principles

* Interest in backend systems, APIs, infrastructure, or developer tooling

* Familiarity with some of the following is helpful, but exact stack match is not required: Python, TypeScript/Node.js, PostgreSQL, Redis, AWS, Docker, Kubernetes, Terraform, and modern CI/CD workflows

* Strong problem-solving skills and willingness to learn quickly

* Comfortable working in a fast-moving, early-stage environment

* Clear written and verbal communication skills

* Fluent English required

Bonus

* Internship, startup, or side-project experience

* Interest in LLM APIs or AI infrastructure

* Experience building developer tools, backend services, or cloud-based systems

* Experience with high-throughput systems such as streaming platforms, real-time APIs, or distributed backend infrastructure at companies with a clear India presence, like Netflix, Stripe, or Datadog. Experience with LLM providers or GPU cloud platforms operating in India, such as OpenAI, Together AI, Fireworks AI, or CoreWeave, is also highly relevant.

FAQs:

What is Concentrate AI?

Concentrate AI is the platform for accessing and managing AI at scale. It gives teams one OpenAI-compatible API to access leading models, choose the best model for each use case, and operate reliably with automatic failover, anomaly detection, and spend visibility.

How is Concentrate different from OpenRouter?

Concentrate provides multi-provider model access like OpenRouter, but is designed for teams running AI at scale. It goes beyond access with automatic failover, anomaly detection, spend visibility, and the controls companies need to manage production AI reliably. Additionally, we don't charge an additional platform fee.

How is Concentrate different from LiteLLM?

LiteLLM is a developer tool for routing model calls. Concentrate is a managed platform for accessing and managing AI at scale, with model selection, failover, anomaly detection, spend visibility, and consolidated billing built in.

Does Concentrate charge a platform fee?

No. Concentrate does not charge a platform fee. Teams get access to routing, failover, anomaly detection, and spend visibility without an added software fee.

Why do teams use Concentrate?

Teams use Concentrate to access the best models through one API, improve reliability with automatic failover, detect issues early with anomaly detection, and keep AI spend visible and under control as usage scales.