Senior Full-Stack Software Engineer, AI & Data

Everbridge; Bengaluru; On-site; INR 1,200,000 - 2,000,000; Full time

Job summary

Everbridge is seeking a versatile software engineer to build internal tools, services, and interfaces across the stack, from backend to frontend, with strong emphasis on AI integration.

You will work on a variety of projects, shipping end-to-end solutions, partnering with the Applied AI Engineer to turn models into product features, and delivering reliable software with broad problem-solving scope.

Qualifications

• Proven experience shipping end-to-end internal tools.
• Strong full-stack skills across backend APIs and frontend.
• Experience shipping LLM-powered features in production.
• Portfolio of varied internal systems built end to end.

Responsibilities

• Design and ship internal tools, services, and interfaces.
• Integrate AI capabilities into products with reliable delivery.
• Collaborate with AI engineers and cross-functional teams.
• Own features from idea to production and monitor impact.

Skills

Full-stack engineering

Range and adaptability

AI tool familiarity

People oriented communicator

Data fluency

Autonomy in small team

Tools

Python

TypeScript

APIs

LLM APIs

CI/CD

Containers

Infrastructure-as-code

Job description

About the team

We’re the company’s internal AI & Data team — a small, fast-moving pod. Our mission is to make the rest of the company dramatically more capable, using AI and Data to automate work, unlock insight, and build the tools and systems teams rely on.

This role is the versatile builder who turns all of that into working software — and who can pick up whatever the moment demands.

The role

You’re a builder, full stop. You’ll work on many things — internal tools, services, interfaces, integrations, automation. You’ll move fluidly between them as priorities shift. This is deliberately a broad, flexible seat: we’re not hiring for one stack or one problem, we’re hiring someone who can build whatever the company needs next and do it well.

AI is a tool in your kit, not the thing you specialize in. You don’t need to design retrieval systems or agents from scratch — but you’re comfortable working with them, wiring them into products, and shipping the software around them. Your superpower is range and reliability: you take things from idea to shipped, across the stack, without needing a narrow lane.

• Build across the stack. Design and ship internal tools and systems end to end — back-end services and APIs, front-end interfaces, data integrations, and the deployment and infrastructure glue that makes them real.
• Work on many things. Move between projects and problem types as priorities shift — one week a workflow-automation tool, the next an internal dashboard, the next helping ship an AI-powered feature. Breadth is the point.
• Put AI to work. Integrate the LLM, RAG, and agent capabilities the team builds into usable software, partnering closely with the Applied AI Engineer to turn intelligence into product.
• Enable the company. Sit with teams across the business, understand their problems, and build the right solution — measured by how much more effective you make everyone else.
• Ship reliably. Own what you build through to production and beyond, with the quality and judgment to know when good-enough-shipped beats perfect-delayed.

• Strong full-stack engineering. You write production-grade code and build comfortably across the stack — back end, APIs, and front end (e.g. Python and/or TypeScript with a modern web framework). You’re not boxed into one layer.
• Range and adaptability. A track record of picking up unfamiliar problems and shipping — you’re energized by variety, not thrown by it.
• Comfort with AI as a tool. You’ve worked extensively with AI — integrating APIs, building features on top of models — even if AI isn’t your specialty.
• A builder who talks to people. You can understand a non-technical team’s problem and turn it into a working solution. Internal enablement rewards engineers who listen as well as they build.
• Data fluency. Enough comfort with pipelines, databases, and structured/unstructured data to work directly with what your software touches.
• Autonomy in a small team. You thrive without heavy process, set your own direction, and are happy wearing whatever hat the moment calls for.

• Experience building internal tools or platforms that other teams depend on.
• Cloud, DevOps, or deployment experience — CI/CD, containers, infrastructure-as-code.
• Hands-on experience shipping LLM-powered features in production.
• A portfolio of varied systems you’ve built end to end.